Data Mining Versuch Clustering von Pokemon

• Autoren: Manuel Eberhardinger, Johannes Theodoridis

• Dozenten: Manuel Eberhardinger, Florian Rupp

• Studierende: Paul Michels (pm080), Erzan Gashi (eg053), Patryk Gadziomski (pg058)

Abgabe:

• Abzugeben ist das Jupyter Notebook mit dem verlangten Implementierungen und den entsprechenden Ausgaben.
• Das Notebook ist als .ipynb und als .html abzugeben.
• Klausurelevante Fragen sind Dokument "Fragenkatalog Datamining" zu finden.
• Antworten auf Fragen im Notebook, Diskussionen und Beschreibung der Ergebnisse sind optional (aber empfohlen) und werden nicht bewertet.

• Übersicht Data Mining Praktikum

Einführung

Lernziele:

In diesem Versuch sollen Kenntnisse in folgenden Themen vermittelt werden:

• Kennenlernen der Pokemon API
• Abfrage von Pokemon-Merkmalen über die API (Data Collection)
• Erstellen der Features für Pokemons (Feature Extraction + Data Preprocessing)
• Kennenlernen verschiedener Clustering-Algorithmen
• Selektion der aussagekräftigsten Merkmale (Feature Selection)
• Clustering von ähnlichen Pokemons

Vor dem Versuch zu klärende Fragen

Kennenlernen der Pokemon API

In diesem Versuch wird die Pokemon API eingesetzt, um Features für verschiedene Pokemon über den API Endpoint zu bekommen. Die API ist öffentlich verfügbar und man braucht somit keinen Account um Daten abfragen zu können. In diesem Versuch wollen wir keine fertige Library benutzen, die Daten für uns abfragen kann, sondern holen diese Daten mit der requests Python-Bibliothek selber.

1. Machen Sie sich mit der Pokemon API vertraut, speziell mit dem Pokemon endpoint. Diesen finden Sie über das Menü auf der Webseite: Contents -> Pokémon -> Pokémon.
2. Installieren Sie die Python-Bibliothek requests mit pip und führen Sie manuell ein paar beispielhafte Abfragen der Pokemon API durch. Benutzen Sie hierzu die get methode von requests mit einer endpoint URL. Eine Authentifizierung ist nicht notwendig. Versuchen Sie verschiedene IDs oder Namen aus und geben Sie den Statuscode sowie den Inhalt der response als Text oder JSON aus.

import requests as req
from PIL import Image
from io import BytesIO
from tqdm import tqdm
import numpy as np
import pandas as pd
from requests import HTTPError, Timeout, TooManyRedirects
from sklearn.preprocessing import MinMaxScaler
import seaborn as sns
import matplotlib.pyplot as plt
import math
from sklearn.decomposition import PCA

import plotly.express as px
from sklearn.cluster import KMeans
from yellowbrick.cluster import KElbowVisualizer
from sklearn.metrics import silhouette_score

# Diese Einstellung erlaubt uns alle Spalten eines pandas Dataframes auszugeben
pd.set_option('display.max_columns', None)

r = req.get("https://pokeapi.co/api/v2/ability/?limit=20&offset=0")
print(r.status_code)
print(r.text)

r = req.get("https://pokeapi.co/api/v2/pokemon-species/?limit=20&offset=0")
print(r.status_code)
print(r.text)

r = req.get("https://pokeapi.co/api/v2/pokemon-species/143/")
print(r.status_code)
print(r.text)

200
{"count":367,"next":"https://pokeapi.co/api/v2/ability/?offset=20&limit=20","previous":null,"results":[{"name":"stench","url":"https://pokeapi.co/api/v2/ability/1/"},{"name":"drizzle","url":"https://pokeapi.co/api/v2/ability/2/"},{"name":"speed-boost","url":"https://pokeapi.co/api/v2/ability/3/"},{"name":"battle-armor","url":"https://pokeapi.co/api/v2/ability/4/"},{"name":"sturdy","url":"https://pokeapi.co/api/v2/ability/5/"},{"name":"damp","url":"https://pokeapi.co/api/v2/ability/6/"},{"name":"limber","url":"https://pokeapi.co/api/v2/ability/7/"},{"name":"sand-veil","url":"https://pokeapi.co/api/v2/ability/8/"},{"name":"static","url":"https://pokeapi.co/api/v2/ability/9/"},{"name":"volt-absorb","url":"https://pokeapi.co/api/v2/ability/10/"},{"name":"water-absorb","url":"https://pokeapi.co/api/v2/ability/11/"},{"name":"oblivious","url":"https://pokeapi.co/api/v2/ability/12/"},{"name":"cloud-nine","url":"https://pokeapi.co/api/v2/ability/13/"},{"name":"compound-eyes","url":"https://pokeapi.co/api/v2/ability/14/"},{"name":"insomnia","url":"https://pokeapi.co/api/v2/ability/15/"},{"name":"color-change","url":"https://pokeapi.co/api/v2/ability/16/"},{"name":"immunity","url":"https://pokeapi.co/api/v2/ability/17/"},{"name":"flash-fire","url":"https://pokeapi.co/api/v2/ability/18/"},{"name":"shield-dust","url":"https://pokeapi.co/api/v2/ability/19/"},{"name":"own-tempo","url":"https://pokeapi.co/api/v2/ability/20/"}]}
200
{"count":1025,"next":"https://pokeapi.co/api/v2/pokemon-species/?offset=20&limit=20","previous":null,"results":[{"name":"bulbasaur","url":"https://pokeapi.co/api/v2/pokemon-species/1/"},{"name":"ivysaur","url":"https://pokeapi.co/api/v2/pokemon-species/2/"},{"name":"venusaur","url":"https://pokeapi.co/api/v2/pokemon-species/3/"},{"name":"charmander","url":"https://pokeapi.co/api/v2/pokemon-species/4/"},{"name":"charmeleon","url":"https://pokeapi.co/api/v2/pokemon-species/5/"},{"name":"charizard","url":"https://pokeapi.co/api/v2/pokemon-species/6/"},{"name":"squirtle","url":"https://pokeapi.co/api/v2/pokemon-species/7/"},{"name":"wartortle","url":"https://pokeapi.co/api/v2/pokemon-species/8/"},{"name":"blastoise","url":"https://pokeapi.co/api/v2/pokemon-species/9/"},{"name":"caterpie","url":"https://pokeapi.co/api/v2/pokemon-species/10/"},{"name":"metapod","url":"https://pokeapi.co/api/v2/pokemon-species/11/"},{"name":"butterfree","url":"https://pokeapi.co/api/v2/pokemon-species/12/"},{"name":"weedle","url":"https://pokeapi.co/api/v2/pokemon-species/13/"},{"name":"kakuna","url":"https://pokeapi.co/api/v2/pokemon-species/14/"},{"name":"beedrill","url":"https://pokeapi.co/api/v2/pokemon-species/15/"},{"name":"pidgey","url":"https://pokeapi.co/api/v2/pokemon-species/16/"},{"name":"pidgeotto","url":"https://pokeapi.co/api/v2/pokemon-species/17/"},{"name":"pidgeot","url":"https://pokeapi.co/api/v2/pokemon-species/18/"},{"name":"rattata","url":"https://pokeapi.co/api/v2/pokemon-species/19/"},{"name":"raticate","url":"https://pokeapi.co/api/v2/pokemon-species/20/"}]}
200
{"base_happiness":50,"capture_rate":25,"color":{"name":"black","url":"https://pokeapi.co/api/v2/pokemon-color/1/"},"egg_groups":[{"name":"monster","url":"https://pokeapi.co/api/v2/egg-group/1/"}],"evolution_chain":{"url":"https://pokeapi.co/api/v2/evolution-chain/72/"},"evolves_from_species":{"name":"munchlax","url":"https://pokeapi.co/api/v2/pokemon-species/446/"},"flavor_text_entries":[{"flavor_text":"Very lazy. Just\neats and sleeps.\nAs its rotund\fbulk builds, it\nbecomes steadily\nmore slothful.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"red","url":"https://pokeapi.co/api/v2/version/1/"}},{"flavor_text":"Very lazy. Just\neats and sleeps.\nAs its rotund\fbulk builds, it\nbecomes steadily\nmore slothful.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"blue","url":"https://pokeapi.co/api/v2/version/2/"}},{"flavor_text":"Will eat anything,\neven if the food\nhappens to be a\flittle moldy. It\nnever gets an\nupset stomach.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"yellow","url":"https://pokeapi.co/api/v2/version/3/"}},{"flavor_text":"What sounds like\nits cry may ac­\ntually be its\fsnores or the\nrumblings of its\nhungry belly.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"gold","url":"https://pokeapi.co/api/v2/version/4/"}},{"flavor_text":"Its stomach's di­\ngestive juices can\ndissolve any kind\fof poison. It can\neven eat things\noff the ground.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"silver","url":"https://pokeapi.co/api/v2/version/5/"}},{"flavor_text":"This POKéMON's\nstomach is so\nstrong, even\feating moldy or\nrotten food will\nnot affect it.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"crystal","url":"https://pokeapi.co/api/v2/version/6/"}},{"flavor_text":"SNORLAX’s typical day consists of\nnothing more than eating and sleeping.\nIt is such a docile POKéMON that there\fare children who use its expansive belly\nas a place to play.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"ruby","url":"https://pokeapi.co/api/v2/version/7/"}},{"flavor_text":"SNORLAX’s typical day consists of\nnothing more than eating and sleeping.\nIt is such a docile POKéMON that there\fare children who use its expansive belly\nas a place to play.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"sapphire","url":"https://pokeapi.co/api/v2/version/8/"}},{"flavor_text":"SNORLAX’s typical day consists of nothing\nmore than eating and sleeping. It is such\na docile POKéMON that there are children\nwho use its big belly as a place to play.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"emerald","url":"https://pokeapi.co/api/v2/version/9/"}},{"flavor_text":"It is not satisfied unless it eats over 880\npounds of food every day. When it is done\neating, it goes promptly to sleep.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"firered","url":"https://pokeapi.co/api/v2/version/10/"}},{"flavor_text":"Very lazy. Just eats and sleeps. As its\nrotund bulk builds, it becomes steadily\nmore slothful.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"leafgreen","url":"https://pokeapi.co/api/v2/version/11/"}},{"flavor_text":"Its stomach can digest any kind\nof food, even if it happens to be\nmoldy or rotten.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"diamond","url":"https://pokeapi.co/api/v2/version/12/"}},{"flavor_text":"It stops eating only to sleep.\nIt doesn’t feel full unless it\neats nearly 900 pounds a day.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"pearl","url":"https://pokeapi.co/api/v2/version/13/"}},{"flavor_text":"When its belly is full, it becomes\ntoo lethargic to even lift a finger,\nso it is safe to bounce on its belly.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"platinum","url":"https://pokeapi.co/api/v2/version/14/"}},{"flavor_text":"What sounds like its cry may\nactually be its snores or the\nrumblings of its hungry belly.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"heartgold","url":"https://pokeapi.co/api/v2/version/15/"}},{"flavor_text":"Its stomach’s digestive juices can\ndissolve any kind of poison. It can\neven eat things off the ground.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"soulsilver","url":"https://pokeapi.co/api/v2/version/16/"}},{"flavor_text":"Une fois le ventre plein, il est trop\namorphe pour lever le petit doigt.\nSautez sur son ventre!","language":{"name":"fr","url":"https://pokeapi.co/api/v2/language/5/"},"version":{"name":"black","url":"https://pokeapi.co/api/v2/version/17/"}},{"flavor_text":"When its belly is full, it becomes\ntoo lethargic to even lift a finger,\nso it is safe to bounce on its belly.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"black","url":"https://pokeapi.co/api/v2/version/17/"}},{"flavor_text":"Une fois le ventre plein, il est trop\namorphe pour lever le petit doigt.\nSautez sur son ventre!","language":{"name":"fr","url":"https://pokeapi.co/api/v2/language/5/"},"version":{"name":"white","url":"https://pokeapi.co/api/v2/version/18/"}},{"flavor_text":"When its belly is full, it becomes\ntoo lethargic to even lift a finger,\nso it is safe to bounce on its belly.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"white","url":"https://pokeapi.co/api/v2/version/18/"}},{"flavor_text":"When its belly is full, it becomes\ntoo lethargic to even lift a finger,\nso it is safe to bounce on its belly.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"black-2","url":"https://pokeapi.co/api/v2/version/21/"}},{"flavor_text":"When its belly is full, it becomes\ntoo lethargic to even lift a finger,\nso it is safe to bounce on its belly.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"white-2","url":"https://pokeapi.co/api/v2/version/22/"}},{"flavor_text":"１にちに　たべものを　４００キロ\nたべないと　きが　すまない。\nたべおわると　ねむってしまう。","language":{"name":"ja-Hrkt","url":"https://pokeapi.co/api/v2/language/1/"},"version":{"name":"x","url":"https://pokeapi.co/api/v2/version/23/"}},{"flavor_text":"하루에 먹을 것을 400kg\n먹지 않으면 성에 차지 않는다.\n다 먹으면 잠이 들어 버린다.","language":{"name":"ko","url":"https://pokeapi.co/api/v2/language/3/"},"version":{"name":"x","url":"https://pokeapi.co/api/v2/version/23/"}},{"flavor_text":"Ronflex n’est pas satisfait tant qu’il n’a pas avalé ses\n400 kg de nourriture quotidienne. Dès qu’il a fini de\nmanger, il commence une sieste pour digérer.","language":{"name":"fr","url":"https://pokeapi.co/api/v2/language/5/"},"version":{"name":"x","url":"https://pokeapi.co/api/v2/version/23/"}},{"flavor_text":"Es ist erst satt, wenn es über 400 kg Nahrung am\nTag gefressen hat. Ist es mit dem Essen fertig,\nschläft es sofort ein.","language":{"name":"de","url":"https://pokeapi.co/api/v2/language/6/"},"version":{"name":"x","url":"https://pokeapi.co/api/v2/version/23/"}},{"flavor_text":"No se encuentra satisfecho hasta que no se come\n400 kg de comida cada día. Cuando acaba de comer,\nse queda dormido.","language":{"name":"es","url":"https://pokeapi.co/api/v2/language/7/"},"version":{"name":"x","url":"https://pokeapi.co/api/v2/version/23/"}},{"flavor_text":"Dopo aver trangugiato i suoi immancabili 400 kg di\ncibo quotidiani, cade in un sonno profondo.","language":{"name":"it","url":"https://pokeapi.co/api/v2/language/8/"},"version":{"name":"x","url":"https://pokeapi.co/api/v2/version/23/"}},{"flavor_text":"It is not satisfied unless it eats over 880 pounds of\nfood every day. When it is done eating, it goes\npromptly to sleep.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"x","url":"https://pokeapi.co/api/v2/version/23/"}},{"flavor_text":"１日に　食べ物を　４００キロ\n食べないと　気がすまない。\n食べ終わると　眠ってしまう。","language":{"name":"ja","url":"https://pokeapi.co/api/v2/language/11/"},"version":{"name":"x","url":"https://pokeapi.co/api/v2/version/23/"}},{"flavor_text":"カビが　はえていても　くさっていても\nだいじょうぶ。カビゴンの　いぶくろは\nなんでも　しょうかできるのだ。","language":{"name":"ja-Hrkt","url":"https://pokeapi.co/api/v2/language/1/"},"version":{"name":"y","url":"https://pokeapi.co/api/v2/version/24/"}},{"flavor_text":"곰팡이가 피어 있거나 상했어도\n괜찮다. 잠만보의 위는\n무엇이든 소화시킬 수 있다.","language":{"name":"ko","url":"https://pokeapi.co/api/v2/language/3/"},"version":{"name":"y","url":"https://pokeapi.co/api/v2/version/24/"}},{"flavor_text":"Son estomac peut digérer n’importe quel type de\nnourriture, même quand elle est moisie ou pourrie.","language":{"name":"fr","url":"https://pokeapi.co/api/v2/language/5/"},"version":{"name":"y","url":"https://pokeapi.co/api/v2/version/24/"}},{"flavor_text":"Sein Magen kann jede Art von Nahrung verdauen,\nselbst wenn sie verschimmelt und verdorben ist.","language":{"name":"de","url":"https://pokeapi.co/api/v2/language/6/"},"version":{"name":"y","url":"https://pokeapi.co/api/v2/version/24/"}},{"flavor_text":"Su estómago puede digerir todo tipo de comida,\nincluso si está podrida o mohosa.","language":{"name":"es","url":"https://pokeapi.co/api/v2/language/7/"},"version":{"name":"y","url":"https://pokeapi.co/api/v2/version/24/"}},{"flavor_text":"Il suo stomaco riesce a digerire qualsiasi tipo di cibo,\nanche se marcio o ammuffito.","language":{"name":"it","url":"https://pokeapi.co/api/v2/language/8/"},"version":{"name":"y","url":"https://pokeapi.co/api/v2/version/24/"}},{"flavor_text":"Its stomach can digest any kind of food,\neven if it happens to be moldy or rotten.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"y","url":"https://pokeapi.co/api/v2/version/24/"}},{"flavor_text":"カビが　生えていても　腐っていても\n大丈夫。カビゴンの　胃袋は\nなんでも　消化できるのだ。","language":{"name":"ja","url":"https://pokeapi.co/api/v2/language/11/"},"version":{"name":"y","url":"https://pokeapi.co/api/v2/version/24/"}},{"flavor_text":"たべて　ねての　くりかえしで　１にちが　おわる。\nおおきな　おなかの　うえを　あそびばに　している\nこどもたちも　いるほど　おとなしい　ポケモンだ。","language":{"name":"ja-Hrkt","url":"https://pokeapi.co/api/v2/language/1/"},"version":{"name":"omega-ruby","url":"https://pokeapi.co/api/v2/version/25/"}},{"flavor_text":"먹고 자는 것을 반복하다 하루가 끝난다.\n큰 배 위를 놀이터로 삼은 아이들이\n있을 정도로 얌전한 포켓몬이다.","language":{"name":"ko","url":"https://pokeapi.co/api/v2/language/3/"},"version":{"name":"omega-ruby","url":"https://pokeapi.co/api/v2/version/25/"}},{"flavor_text":"Les journées de Ronflex se résument aux repas et aux siestes.\nC’est un Pokémon tellement gentil que les enfants n’hésitent\npas à jouer sur son énorme ventre.","language":{"name":"fr","url":"https://pokeapi.co/api/v2/language/5/"},"version":{"name":"omega-ruby","url":"https://pokeapi.co/api/v2/version/25/"}},{"flavor_text":"Der typische Tagesablauf von Relaxo besteht lediglich aus\nEssen und Schlafen. Es ist so zutraulich, dass Kinder seinen\ndicken Bauch als Platz zum Spielen nutzen.","language":{"name":"de","url":"https://pokeapi.co/api/v2/language/6/"},"version":{"name":"omega-ruby","url":"https://pokeapi.co/api/v2/version/25/"}},{"flavor_text":"Un día cualquiera en la vida de Snorlax consiste en comer y\ndormir. Es un Pokémon tan dócil que es fácil ver niños usando\nla gran panza que tiene como lugar de juegos.","language":{"name":"es","url":"https://pokeapi.co/api/v2/language/7/"},"version":{"name":"omega-ruby","url":"https://pokeapi.co/api/v2/version/25/"}},{"flavor_text":"La giornata tipica di Snorlax consiste in nient’altro che\nmangiare e dormire. È un Pokémon così docile che\ni bambini usano la sua enorme pancia come parco giochi.","language":{"name":"it","url":"https://pokeapi.co/api/v2/language/8/"},"version":{"name":"omega-ruby","url":"https://pokeapi.co/api/v2/version/25/"}},{"flavor_text":"Snorlax’s typical day consists of nothing more than eating and\nsleeping. It is such a docile Pokémon that there are children\nwho use its expansive belly as a place to play.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"omega-ruby","url":"https://pokeapi.co/api/v2/version/25/"}},{"flavor_text":"食べて　寝ての　繰り返しで　１日が　終わる。\n大きな　お腹の　上を　遊び場に　している\n子どもたちも　いるほど　大人しい　ポケモンだ。","language":{"name":"ja","url":"https://pokeapi.co/api/v2/language/11/"},"version":{"name":"omega-ruby","url":"https://pokeapi.co/api/v2/version/25/"}},{"flavor_text":"たべて　ねての　くりかえしで　１にちが　おわる。\nおおきな　おなかの　うえを　あそびばに　している\nこどもたちも　いるほど　おとなしい　ポケモンだ。","language":{"name":"ja-Hrkt","url":"https://pokeapi.co/api/v2/language/1/"},"version":{"name":"alpha-sapphire","url":"https://pokeapi.co/api/v2/version/26/"}},{"flavor_text":"먹고 자는 것을 반복하다 하루가 끝난다.\n큰 배 위를 놀이터로 삼은 아이들이\n있을 정도로 얌전한 포켓몬이다.","language":{"name":"ko","url":"https://pokeapi.co/api/v2/language/3/"},"version":{"name":"alpha-sapphire","url":"https://pokeapi.co/api/v2/version/26/"}},{"flavor_text":"Les journées de Ronflex se résument aux repas et aux siestes.\nC’est un Pokémon tellement gentil que les enfants n’hésitent\npas à jouer sur son énorme ventre.","language":{"name":"fr","url":"https://pokeapi.co/api/v2/language/5/"},"version":{"name":"alpha-sapphire","url":"https://pokeapi.co/api/v2/version/26/"}},{"flavor_text":"Der typische Tagesablauf von Relaxo besteht lediglich aus\nEssen und Schlafen. Es ist so zutraulich, dass Kinder seinen\ndicken Bauch als Platz zum Spielen nutzen.","language":{"name":"de","url":"https://pokeapi.co/api/v2/language/6/"},"version":{"name":"alpha-sapphire","url":"https://pokeapi.co/api/v2/version/26/"}},{"flavor_text":"Un día cualquiera en la vida de Snorlax consiste en comer y\ndormir. Es un Pokémon tan dócil que es fácil ver niños usando\nla gran panza que tiene como lugar de juegos.","language":{"name":"es","url":"https://pokeapi.co/api/v2/language/7/"},"version":{"name":"alpha-sapphire","url":"https://pokeapi.co/api/v2/version/26/"}},{"flavor_text":"La giornata tipica di Snorlax consiste in nient’altro che\nmangiare e dormire. È un Pokémon così docile che\ni bambini usano la sua enorme pancia come parco giochi.","language":{"name":"it","url":"https://pokeapi.co/api/v2/language/8/"},"version":{"name":"alpha-sapphire","url":"https://pokeapi.co/api/v2/version/26/"}},{"flavor_text":"Snorlax’s typical day consists of nothing more than eating and\nsleeping. It is such a docile Pokémon that there are children\nwho use its expansive belly as a place to play.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"alpha-sapphire","url":"https://pokeapi.co/api/v2/version/26/"}},{"flavor_text":"食べて　寝ての　繰り返しで　１日が　終わる。\n大きな　お腹の　上を　遊び場に　している\n子どもたちも　いるほど　大人しい　ポケモンだ。","language":{"name":"ja","url":"https://pokeapi.co/api/v2/language/11/"},"version":{"name":"alpha-sapphire","url":"https://pokeapi.co/api/v2/version/26/"}},{"flavor_text":"さいきょうの　いぶくろを　もつと\nいわれる。　ベトベトンの　どくでさえ\nカビゴンの　したには　スパイスなのだ。","language":{"name":"ja-Hrkt","url":"https://pokeapi.co/api/v2/language/1/"},"version":{"name":"sun","url":"https://pokeapi.co/api/v2/version/27/"}},{"flavor_text":"최강의 위를 가졌다고\n전해진다. 질뻐기의 독조차\n잠만보의 혀에는 향신료인 셈이다.","language":{"name":"ko","url":"https://pokeapi.co/api/v2/language/3/"},"version":{"name":"sun","url":"https://pokeapi.co/api/v2/version/27/"}},{"flavor_text":"據說擁有最強的胃袋。\n就連臭臭泥的毒對卡比獸的\n舌頭來說也只是調味料而已。","language":{"name":"zh-Hant","url":"https://pokeapi.co/api/v2/language/4/"},"version":{"name":"sun","url":"https://pokeapi.co/api/v2/version/27/"}},{"flavor_text":"On dit que son estomac n’a pas d’égal.\nMême les toxines de Grotadmorv ne sont rien\nde plus qu’un assaisonnement épicé pour lui.","language":{"name":"fr","url":"https://pokeapi.co/api/v2/language/5/"},"version":{"name":"sun","url":"https://pokeapi.co/api/v2/version/27/"}},{"flavor_text":"Es heißt, sein Magen sei an Widerstandsfähigkeit\nnicht zu übertreffen. Für Relaxo ist selbst das\nGift eines Sleimok nicht mehr als ein Würzstoff.","language":{"name":"de","url":"https://pokeapi.co/api/v2/language/6/"},"version":{"name":"sun","url":"https://pokeapi.co/api/v2/version/27/"}},{"flavor_text":"Su estómago es exageradamente resistente.\nTanto que podría condimentar su comida con el\nveneno de Muk sin inmutarse.","language":{"name":"es","url":"https://pokeapi.co/api/v2/language/7/"},"version":{"name":"sun","url":"https://pokeapi.co/api/v2/version/27/"}},{"flavor_text":"Sembra che abbia uno stomaco fortissimo.\nIl fluido tossico di Muk per lui è solo un\ncondimento un po’ speziato.","language":{"name":"it","url":"https://pokeapi.co/api/v2/language/8/"},"version":{"name":"sun","url":"https://pokeapi.co/api/v2/version/27/"}},{"flavor_text":"Its stomach is said to be incomparably strong.\nEven Muk’s poison is nothing more than a hint of\nspice on Snorlax’s tongue.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"sun","url":"https://pokeapi.co/api/v2/version/27/"}},{"flavor_text":"最強の　胃袋を　持つと\nいわれる。　ベトベトンの　毒でさえ\nカビゴンの　舌には　スパイスなのだ。","language":{"name":"ja","url":"https://pokeapi.co/api/v2/language/11/"},"version":{"name":"sun","url":"https://pokeapi.co/api/v2/version/27/"}},{"flavor_text":"据说拥有最强的胃袋。\n就连臭臭泥的毒对卡比兽的\n舌头来说也只是调味料而已。","language":{"name":"zh-Hans","url":"https://pokeapi.co/api/v2/language/12/"},"version":{"name":"sun","url":"https://pokeapi.co/api/v2/version/27/"}},{"flavor_text":"まいにち　４００キロの　エサを　くう。\nくってるはじから　いねむりを　はじめ\nねむったままでも　くっている。","language":{"name":"ja-Hrkt","url":"https://pokeapi.co/api/v2/language/1/"},"version":{"name":"moon","url":"https://pokeapi.co/api/v2/version/28/"}},{"flavor_text":"매일 400kg의 먹이를 먹는다.\n먹기 시작할 때부터 졸기 시작하여\n자는 동안에도 먹고 있다.","language":{"name":"ko","url":"https://pokeapi.co/api/v2/language/3/"},"version":{"name":"moon","url":"https://pokeapi.co/api/v2/version/28/"}},{"flavor_text":"每天要吃下４００公斤的食物。\n一開始吃就會打瞌睡，\n即使是睡著的狀態也會繼續吃。","language":{"name":"zh-Hant","url":"https://pokeapi.co/api/v2/language/4/"},"version":{"name":"moon","url":"https://pokeapi.co/api/v2/version/28/"}},{"flavor_text":"Il mange 400 kg de nourriture par jour.\nIl arrive que sa sieste commence pendant qu’il\nmange, mais il continue à manger même endormi.","language":{"name":"fr","url":"https://pokeapi.co/api/v2/language/5/"},"version":{"name":"moon","url":"https://pokeapi.co/api/v2/version/28/"}},{"flavor_text":"Es nimmt jeden Tag 400 kg an Nahrung zu sich.\nEs schläft beim Essen ein und frisst selbst im\nSchlaf noch weiter.","language":{"name":"de","url":"https://pokeapi.co/api/v2/language/6/"},"version":{"name":"moon","url":"https://pokeapi.co/api/v2/version/28/"}},{"flavor_text":"Engulle 400 kg de comida a diario. Si le entra\nsueño cuando está alimentándose, sigue\ncomiendo mientras echa una cabezada.","language":{"name":"es","url":"https://pokeapi.co/api/v2/language/7/"},"version":{"name":"moon","url":"https://pokeapi.co/api/v2/version/28/"}},{"flavor_text":"Ogni giorno mangia 400 kg di cibo.\nAppena inizia a mangiare, si addormenta, ma\ncontinua anche mentre dorme.","language":{"name":"it","url":"https://pokeapi.co/api/v2/language/8/"},"version":{"name":"moon","url":"https://pokeapi.co/api/v2/version/28/"}},{"flavor_text":"It eats nearly 900 pounds of food every day.\nIt starts nodding off while eating—and continues\nto eat even while it’s asleep.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"moon","url":"https://pokeapi.co/api/v2/version/28/"}},{"flavor_text":"毎日　４００キロの　餌を　喰う。\n喰ってるはじから　居眠りを　はじめ\n眠ったままでも　喰っている。","language":{"name":"ja","url":"https://pokeapi.co/api/v2/language/11/"},"version":{"name":"moon","url":"https://pokeapi.co/api/v2/version/28/"}},{"flavor_text":"每天要吃下４００公斤的食物。\n开吃之后，就会开始打瞌睡，\n即使是睡着的状态也会继续吃。","language":{"name":"zh-Hans","url":"https://pokeapi.co/api/v2/language/12/"},"version":{"name":"moon","url":"https://pokeapi.co/api/v2/version/28/"}},{"flavor_text":"くうか　ねるかしか　していないが\nなにかの　きっかけで　ほんきをだすと\nすごい　パワーを　はっきするらしい。","language":{"name":"ja-Hrkt","url":"https://pokeapi.co/api/v2/language/1/"},"version":{"name":"ultra-sun","url":"https://pokeapi.co/api/v2/version/29/"}},{"flavor_text":"먹거나 자는 것밖에 안 하지만\n일단 진심을 다하게 되면\n굉장한 파워를 발휘하는 듯하다.","language":{"name":"ko","url":"https://pokeapi.co/api/v2/language/3/"},"version":{"name":"ultra-sun","url":"https://pokeapi.co/api/v2/version/29/"}},{"flavor_text":"平常不是吃就是睡，但如果\n因某種原因認真起來的話，\n似乎就會發揮驚人的力量。","language":{"name":"zh-Hant","url":"https://pokeapi.co/api/v2/language/4/"},"version":{"name":"ultra-sun","url":"https://pokeapi.co/api/v2/version/29/"}},{"flavor_text":"Il ne fait pas grand-chose à part manger\net dormir, mais gare à lui quand il décide\nde passer à l’action !","language":{"name":"fr","url":"https://pokeapi.co/api/v2/language/5/"},"version":{"name":"ultra-sun","url":"https://pokeapi.co/api/v2/version/29/"}},{"flavor_text":"Wenn es nicht gerade frisst, dann schläft es.\nMacht es aber einmal Ernst, ist Schluss mit\nlustig.","language":{"name":"de","url":"https://pokeapi.co/api/v2/language/6/"},"version":{"name":"ultra-sun","url":"https://pokeapi.co/api/v2/version/29/"}},{"flavor_text":"Poco hace aparte de comer y dormir, pero\npuede desatar una fuerza arrolladora si le da\nun arrebato.","language":{"name":"es","url":"https://pokeapi.co/api/v2/language/7/"},"version":{"name":"ultra-sun","url":"https://pokeapi.co/api/v2/version/29/"}},{"flavor_text":"Non fa altro che mangiare e dormire, ma quando\ndecide di fare sul serio tira fuori una potenza\neccezionale.","language":{"name":"it","url":"https://pokeapi.co/api/v2/language/8/"},"version":{"name":"ultra-sun","url":"https://pokeapi.co/api/v2/version/29/"}},{"flavor_text":"It doesn’t do anything other than eat and sleep.\nWhen prompted to make a serious effort,\nthough, it apparently displays awesome power.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"ultra-sun","url":"https://pokeapi.co/api/v2/version/29/"}},{"flavor_text":"食うか　寝るかしか　していないが\nなにかの　きっかけで　本気を出すと\n凄い　パワーを　発揮するらしい。","language":{"name":"ja","url":"https://pokeapi.co/api/v2/language/11/"},"version":{"name":"ultra-sun","url":"https://pokeapi.co/api/v2/version/29/"}},{"flavor_text":"平时不是吃就是睡，但如果因\n某些事而认真起来的话，\n好像就会发挥出惊人的力量。","language":{"name":"zh-Hans","url":"https://pokeapi.co/api/v2/language/12/"},"version":{"name":"ultra-sun","url":"https://pokeapi.co/api/v2/version/29/"}},{"flavor_text":"たべること　いがいに　きょうみが\nない。　ひるねちゅうに　おなかのうえに\nのっかっても　まるで　きにしないぞ。","language":{"name":"ja-Hrkt","url":"https://pokeapi.co/api/v2/language/1/"},"version":{"name":"ultra-moon","url":"https://pokeapi.co/api/v2/version/30/"}},{"flavor_text":"먹는 것 외에는 관심이 없다.\n낮잠 중에 배 위에 올라타도\n전혀 신경 쓰지 않는다.","language":{"name":"ko","url":"https://pokeapi.co/api/v2/language/3/"},"version":{"name":"ultra-moon","url":"https://pokeapi.co/api/v2/version/30/"}},{"flavor_text":"就只對吃感興趣。\n當牠睡午覺時，就算站上牠的肚子，\n牠也絲毫不會在意。","language":{"name":"zh-Hant","url":"https://pokeapi.co/api/v2/language/4/"},"version":{"name":"ultra-moon","url":"https://pokeapi.co/api/v2/version/30/"}},{"flavor_text":"La seule chose qui l’intéresse, c’est manger.\nIl ne se réveille même pas si quelqu’un grimpe\nsur son ventre pendant qu’il fait une sieste.","language":{"name":"fr","url":"https://pokeapi.co/api/v2/language/5/"},"version":{"name":"ultra-moon","url":"https://pokeapi.co/api/v2/version/30/"}},{"flavor_text":"Bei ihm geht es immer nur ums Fressen. Macht\nman es sich auf seinem Bauch bequem, während\nes döst, ist es ihm daher egal.","language":{"name":"de","url":"https://pokeapi.co/api/v2/language/6/"},"version":{"name":"ultra-moon","url":"https://pokeapi.co/api/v2/version/30/"}},{"flavor_text":"Solo piensa en comer y lo demás no le interesa.\nCuando duerme la siesta, no se inmuta ni aunque\nalguien se le tumbe en la barriga.","language":{"name":"es","url":"https://pokeapi.co/api/v2/language/7/"},"version":{"name":"ultra-moon","url":"https://pokeapi.co/api/v2/version/30/"}},{"flavor_text":"Il suo unico interesse è il cibo. Quando fa\nla sua pennichella pomeridiana, non si accorge\nnemmeno se qualcuno gli sale sulla pancia.","language":{"name":"it","url":"https://pokeapi.co/api/v2/language/8/"},"version":{"name":"ultra-moon","url":"https://pokeapi.co/api/v2/version/30/"}},{"flavor_text":"It has no interest in anything other than eating.\nEven if you climb up on its stomach while it’s\nnapping, it doesn’t seem to mind at all!","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"ultra-moon","url":"https://pokeapi.co/api/v2/version/30/"}},{"flavor_text":"食べること　以外に　興味が\nない。　昼寝中に　お腹の上に\n乗っかっても　まるで　気にしないぞ。","language":{"name":"ja","url":"https://pokeapi.co/api/v2/language/11/"},"version":{"name":"ultra-moon","url":"https://pokeapi.co/api/v2/version/30/"}},{"flavor_text":"只对吃感兴趣。\n即便是在它睡午觉时跳到\n它肚子上，它也毫不在意。","language":{"name":"zh-Hans","url":"https://pokeapi.co/api/v2/language/12/"},"version":{"name":"ultra-moon","url":"https://pokeapi.co/api/v2/version/30/"}},{"flavor_text":"たしょう　カビが　はえていても\nきにしないで　たべまくる。\nおなかを　こわしたりもしない。","language":{"name":"ja-Hrkt","url":"https://pokeapi.co/api/v2/language/1/"},"version":{"name":"lets-go-pikachu","url":"https://pokeapi.co/api/v2/version/31/"}},{"flavor_text":"약간 곰팡이가 피어 있어도\n신경 쓰지 않고 먹어 치운다.\n배탈이 나는 일도 없다.","language":{"name":"ko","url":"https://pokeapi.co/api/v2/language/3/"},"version":{"name":"lets-go-pikachu","url":"https://pokeapi.co/api/v2/version/31/"}},{"flavor_text":"就算食物有點發霉，\n也會毫不在意地大吃特吃，\n從不會因此吃壞肚子。","language":{"name":"zh-Hant","url":"https://pokeapi.co/api/v2/language/4/"},"version":{"name":"lets-go-pikachu","url":"https://pokeapi.co/api/v2/version/31/"}},{"flavor_text":"Il peut tout manger, même de la nourriture\navariée. Il n’a jamais mal au ventre.","language":{"name":"fr","url":"https://pokeapi.co/api/v2/language/5/"},"version":{"name":"lets-go-pikachu","url":"https://pokeapi.co/api/v2/version/31/"}},{"flavor_text":"Relaxo verschlingt sogar Nahrung, auf der sich\netwas Schimmel gebildet hat. Trotzdem verdirbt\nes sich nie den Magen.","language":{"name":"de","url":"https://pokeapi.co/api/v2/language/6/"},"version":{"name":"lets-go-pikachu","url":"https://pokeapi.co/api/v2/version/31/"}},{"flavor_text":"Engulle cualquier cosa, incluso comida mohosa,\nsin que esto le cause dolor de estómago.","language":{"name":"es","url":"https://pokeapi.co/api/v2/language/7/"},"version":{"name":"lets-go-pikachu","url":"https://pokeapi.co/api/v2/version/31/"}},{"flavor_text":"Mangia qualsiasi cosa gli capiti a tiro, anche\nse ammuffita, senza mai soffrire di mal di pancia.","language":{"name":"it","url":"https://pokeapi.co/api/v2/language/8/"},"version":{"name":"lets-go-pikachu","url":"https://pokeapi.co/api/v2/version/31/"}},{"flavor_text":"Will eat anything, even if the food happens to\nbe a little moldy. It never gets an upset stomach.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"lets-go-pikachu","url":"https://pokeapi.co/api/v2/version/31/"}},{"flavor_text":"多少　カビが　生えていても\n気にしないで　食べまくる。\nお腹を　壊したりもしない。","language":{"name":"ja","url":"https://pokeapi.co/api/v2/language/11/"},"version":{"name":"lets-go-pikachu","url":"https://pokeapi.co/api/v2/version/31/"}},{"flavor_text":"就算是有些发霉的食物\n也能毫不在意地吃完，\n并且完全不会吃坏肚子。","language":{"name":"zh-Hans","url":"https://pokeapi.co/api/v2/language/12/"},"version":{"name":"lets-go-pikachu","url":"https://pokeapi.co/api/v2/version/31/"}},{"flavor_text":"たしょう　カビが　はえていても\nきにしないで　たべまくる。\nおなかを　こわしたりもしない。","language":{"name":"ja-Hrkt","url":"https://pokeapi.co/api/v2/language/1/"},"version":{"name":"lets-go-eevee","url":"https://pokeapi.co/api/v2/version/32/"}},{"flavor_text":"약간 곰팡이가 피어 있어도\n신경 쓰지 않고 먹어 치운다.\n배탈이 나는 일도 없다.","language":{"name":"ko","url":"https://pokeapi.co/api/v2/language/3/"},"version":{"name":"lets-go-eevee","url":"https://pokeapi.co/api/v2/version/32/"}},{"flavor_text":"就算食物有點發霉，\n也會毫不在意地大吃特吃，\n從不會因此吃壞肚子。","language":{"name":"zh-Hant","url":"https://pokeapi.co/api/v2/language/4/"},"version":{"name":"lets-go-eevee","url":"https://pokeapi.co/api/v2/version/32/"}},{"flavor_text":"Il peut tout manger, même de la nourriture\navariée. Il n’a jamais mal au ventre.","language":{"name":"fr","url":"https://pokeapi.co/api/v2/language/5/"},"version":{"name":"lets-go-eevee","url":"https://pokeapi.co/api/v2/version/32/"}},{"flavor_text":"Relaxo verschlingt sogar Nahrung, auf der sich\netwas Schimmel gebildet hat. Trotzdem verdirbt\nes sich nie den Magen.","language":{"name":"de","url":"https://pokeapi.co/api/v2/language/6/"},"version":{"name":"lets-go-eevee","url":"https://pokeapi.co/api/v2/version/32/"}},{"flavor_text":"Engulle cualquier cosa, incluso comida mohosa,\nsin que esto le cause dolor de estómago.","language":{"name":"es","url":"https://pokeapi.co/api/v2/language/7/"},"version":{"name":"lets-go-eevee","url":"https://pokeapi.co/api/v2/version/32/"}},{"flavor_text":"Mangia qualsiasi cosa gli capiti a tiro, anche\nse ammuffita, senza mai soffrire di mal di pancia.","language":{"name":"it","url":"https://pokeapi.co/api/v2/language/8/"},"version":{"name":"lets-go-eevee","url":"https://pokeapi.co/api/v2/version/32/"}},{"flavor_text":"Will eat anything, even if the food happens to\nbe a little moldy. It never gets an upset stomach.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"lets-go-eevee","url":"https://pokeapi.co/api/v2/version/32/"}},{"flavor_text":"多少　カビが　生えていても\n気にしないで　食べまくる。\nお腹を　壊したりもしない。","language":{"name":"ja","url":"https://pokeapi.co/api/v2/language/11/"},"version":{"name":"lets-go-eevee","url":"https://pokeapi.co/api/v2/version/32/"}},{"flavor_text":"就算是有些发霉的食物\n也能毫不在意地吃完，\n并且完全不会吃坏肚子。","language":{"name":"zh-Hans","url":"https://pokeapi.co/api/v2/language/12/"},"version":{"name":"lets-go-eevee","url":"https://pokeapi.co/api/v2/version/32/"}},{"flavor_text":"１にちに　たべものを　４００キロ\nたべないと　きが　すまない。\nたべおわると　ねむってしまう。","language":{"name":"ja-Hrkt","url":"https://pokeapi.co/api/v2/language/1/"},"version":{"name":"sword","url":"https://pokeapi.co/api/v2/version/33/"}},{"flavor_text":"하루에 400kg의 음식을\n먹지 않으면 성에 차지 않는다.\n다 먹으면 잠이 들어 버린다.","language":{"name":"ko","url":"https://pokeapi.co/api/v2/language/3/"},"version":{"name":"sword","url":"https://pokeapi.co/api/v2/version/33/"}},{"flavor_text":"每天不吃下４００公斤的食物\n絕不會善罷甘休。\n吃飽了就會開始睡覺。","language":{"name":"zh-Hant","url":"https://pokeapi.co/api/v2/language/4/"},"version":{"name":"sword","url":"https://pokeapi.co/api/v2/version/33/"}},{"flavor_text":"Ronflex n’est pas satisfait tant qu’il n’a pas avalé\nses 400 kg de nourriture quotidienne. Dès qu’il\na fini, il commence une sieste pour digérer.","language":{"name":"fr","url":"https://pokeapi.co/api/v2/language/5/"},"version":{"name":"sword","url":"https://pokeapi.co/api/v2/version/33/"}},{"flavor_text":"Es muss über 400 kg Nahrung am Tag fressen,\num satt zu werden. Ist es mit dem Essen fertig,\nschläft es sofort ein.","language":{"name":"de","url":"https://pokeapi.co/api/v2/language/6/"},"version":{"name":"sword","url":"https://pokeapi.co/api/v2/version/33/"}},{"flavor_text":"No se encuentra satisfecho hasta haber ingerido\n400 kg de comida cada día. Cuando acaba de\ncomer, se queda dormido.","language":{"name":"es","url":"https://pokeapi.co/api/v2/language/7/"},"version":{"name":"sword","url":"https://pokeapi.co/api/v2/version/33/"}},{"flavor_text":"Dopo aver trangugiato i suoi immancabili 400 kg\ndi cibo quotidiani, cade in un sonno profondo.","language":{"name":"it","url":"https://pokeapi.co/api/v2/language/8/"},"version":{"name":"sword","url":"https://pokeapi.co/api/v2/version/33/"}},{"flavor_text":"It is not satisfied unless it eats over 880 pounds\nof food every day. When it is done eating, it\ngoes promptly to sleep.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"sword","url":"https://pokeapi.co/api/v2/version/33/"}},{"flavor_text":"１日に　食べ物を　４００キロ\n食べないと　気がすまない。\n食べ終わると　眠ってしまう。","language":{"name":"ja","url":"https://pokeapi.co/api/v2/language/11/"},"version":{"name":"sword","url":"https://pokeapi.co/api/v2/version/33/"}},{"flavor_text":"每天不吃上４００公斤的食物\n心里就不畅快。\n吃饱了就会开始睡觉。","language":{"name":"zh-Hans","url":"https://pokeapi.co/api/v2/language/12/"},"version":{"name":"sword","url":"https://pokeapi.co/api/v2/version/33/"}},{"flavor_text":"がんじょうな　いぶくろは　カビの\nはえたものや　くさったものを\nたべても　こわれることはない。","language":{"name":"ja-Hrkt","url":"https://pokeapi.co/api/v2/language/1/"},"version":{"name":"shield","url":"https://pokeapi.co/api/v2/version/34/"}},{"flavor_text":"위장이 튼튼해서\n곰팡이가 피거나 썩은 것을\n먹어도 배탈이 나지 않는다.","language":{"name":"ko","url":"https://pokeapi.co/api/v2/language/3/"},"version":{"name":"shield","url":"https://pokeapi.co/api/v2/version/34/"}},{"flavor_text":"擁有一個結實的胃，\n即使吃了發霉腐爛的東西，\n也完全不會吃壞肚子。","language":{"name":"zh-Hant","url":"https://pokeapi.co/api/v2/language/4/"},"version":{"name":"shield","url":"https://pokeapi.co/api/v2/version/34/"}},{"flavor_text":"L’estomac de ce Pokémon est si résistant\nqu’il peut même digérer de la nourriture avariée.","language":{"name":"fr","url":"https://pokeapi.co/api/v2/language/5/"},"version":{"name":"shield","url":"https://pokeapi.co/api/v2/version/34/"}},{"flavor_text":"Der Magen dieses Pokémon ist so resistent,\ndass es sogar Verschimmeltes oder Verdorbenes\nessen kann.","language":{"name":"de","url":"https://pokeapi.co/api/v2/language/6/"},"version":{"name":"shield","url":"https://pokeapi.co/api/v2/version/34/"}},{"flavor_text":"Este Pokémon tiene un estómago a prueba de\nbomba, por lo que es capaz de ingerir incluso\ncomida podrida o mohosa.","language":{"name":"es","url":"https://pokeapi.co/api/v2/language/7/"},"version":{"name":"shield","url":"https://pokeapi.co/api/v2/version/34/"}},{"flavor_text":"Ha uno stomaco così resistente che può\ningerire anche cibo ammuffito o avariato.","language":{"name":"it","url":"https://pokeapi.co/api/v2/language/8/"},"version":{"name":"shield","url":"https://pokeapi.co/api/v2/version/34/"}},{"flavor_text":"This Pokémon’s stomach is so strong, even eating\nmoldy or rotten food will not affect it.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"shield","url":"https://pokeapi.co/api/v2/version/34/"}},{"flavor_text":"頑丈な　胃袋は　カビの\n生えたものや　腐ったものを\n食べても　壊れることはない。","language":{"name":"ja","url":"https://pokeapi.co/api/v2/language/11/"},"version":{"name":"shield","url":"https://pokeapi.co/api/v2/version/34/"}},{"flavor_text":"拥有一个结实的胃，\n即使吃了发霉腐烂的东西，\n也完全不会吃坏肚子。","language":{"name":"zh-Hans","url":"https://pokeapi.co/api/v2/language/12/"},"version":{"name":"shield","url":"https://pokeapi.co/api/v2/version/34/"}},{"flavor_text":"This glutton appears in villages without warning and devours the\nentirety of their rice granaries—such occurrences have long been\ncounted among the gravest of disasters.","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"version":{"name":"legends-arceus","url":"https://pokeapi.co/api/v2/version/39/"}}],"form_descriptions":[],"forms_switchable":false,"gender_rate":1,"genera":[{"genus":"いねむりポケモン","language":{"name":"ja-Hrkt","url":"https://pokeapi.co/api/v2/language/1/"}},{"genus":"졸음포켓몬","language":{"name":"ko","url":"https://pokeapi.co/api/v2/language/3/"}},{"genus":"瞌睡寶可夢","language":{"name":"zh-Hant","url":"https://pokeapi.co/api/v2/language/4/"}},{"genus":"Pokémon Pionceur","language":{"name":"fr","url":"https://pokeapi.co/api/v2/language/5/"}},{"genus":"Tagträumer-Pokémon","language":{"name":"de","url":"https://pokeapi.co/api/v2/language/6/"}},{"genus":"Pokémon Dormir","language":{"name":"es","url":"https://pokeapi.co/api/v2/language/7/"}},{"genus":"Pokémon Sonno","language":{"name":"it","url":"https://pokeapi.co/api/v2/language/8/"}},{"genus":"Sleeping Pokémon","language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"}},{"genus":"いねむりポケモン","language":{"name":"ja","url":"https://pokeapi.co/api/v2/language/11/"}},{"genus":"瞌睡宝可梦","language":{"name":"zh-Hans","url":"https://pokeapi.co/api/v2/language/12/"}}],"generation":{"name":"generation-i","url":"https://pokeapi.co/api/v2/generation/1/"},"growth_rate":{"name":"slow","url":"https://pokeapi.co/api/v2/growth-rate/1/"},"habitat":{"name":"mountain","url":"https://pokeapi.co/api/v2/pokemon-habitat/4/"},"has_gender_differences":false,"hatch_counter":40,"id":143,"is_baby":false,"is_legendary":false,"is_mythical":false,"name":"snorlax","names":[{"language":{"name":"ja-Hrkt","url":"https://pokeapi.co/api/v2/language/1/"},"name":"カビゴン"},{"language":{"name":"roomaji","url":"https://pokeapi.co/api/v2/language/2/"},"name":"Kabigon"},{"language":{"name":"ko","url":"https://pokeapi.co/api/v2/language/3/"},"name":"잠만보"},{"language":{"name":"zh-Hant","url":"https://pokeapi.co/api/v2/language/4/"},"name":"卡比獸"},{"language":{"name":"fr","url":"https://pokeapi.co/api/v2/language/5/"},"name":"Ronflex"},{"language":{"name":"de","url":"https://pokeapi.co/api/v2/language/6/"},"name":"Relaxo"},{"language":{"name":"es","url":"https://pokeapi.co/api/v2/language/7/"},"name":"Snorlax"},{"language":{"name":"it","url":"https://pokeapi.co/api/v2/language/8/"},"name":"Snorlax"},{"language":{"name":"en","url":"https://pokeapi.co/api/v2/language/9/"},"name":"Snorlax"},{"language":{"name":"ja","url":"https://pokeapi.co/api/v2/language/11/"},"name":"カビゴン"},{"language":{"name":"zh-Hans","url":"https://pokeapi.co/api/v2/language/12/"},"name":"卡比兽"}],"order":175,"pal_park_encounters":[{"area":{"name":"field","url":"https://pokeapi.co/api/v2/pal-park-area/2/"},"base_score":80,"rate":10}],"pokedex_numbers":[{"entry_number":143,"pokedex":{"name":"national","url":"https://pokeapi.co/api/v2/pokedex/1/"}},{"entry_number":143,"pokedex":{"name":"kanto","url":"https://pokeapi.co/api/v2/pokedex/2/"}},{"entry_number":225,"pokedex":{"name":"original-johto","url":"https://pokeapi.co/api/v2/pokedex/3/"}},{"entry_number":113,"pokedex":{"name":"original-sinnoh","url":"https://pokeapi.co/api/v2/pokedex/5/"}},{"entry_number":113,"pokedex":{"name":"extended-sinnoh","url":"https://pokeapi.co/api/v2/pokedex/6/"}},{"entry_number":230,"pokedex":{"name":"updated-johto","url":"https://pokeapi.co/api/v2/pokedex/7/"}},{"entry_number":179,"pokedex":{"name":"conquest-gallery","url":"https://pokeapi.co/api/v2/pokedex/11/"}},{"entry_number":139,"pokedex":{"name":"kalos-central","url":"https://pokeapi.co/api/v2/pokedex/12/"}},{"entry_number":36,"pokedex":{"name":"original-alola","url":"https://pokeapi.co/api/v2/pokedex/16/"}},{"entry_number":36,"pokedex":{"name":"original-melemele","url":"https://pokeapi.co/api/v2/pokedex/17/"}},{"entry_number":43,"pokedex":{"name":"updated-alola","url":"https://pokeapi.co/api/v2/pokedex/21/"}},{"entry_number":43,"pokedex":{"name":"updated-melemele","url":"https://pokeapi.co/api/v2/pokedex/22/"}},{"entry_number":143,"pokedex":{"name":"letsgo-kanto","url":"https://pokeapi.co/api/v2/pokedex/26/"}},{"entry_number":261,"pokedex":{"name":"galar","url":"https://pokeapi.co/api/v2/pokedex/27/"}},{"entry_number":173,"pokedex":{"name":"crown-tundra","url":"https://pokeapi.co/api/v2/pokedex/29/"}},{"entry_number":52,"pokedex":{"name":"hisui","url":"https://pokeapi.co/api/v2/pokedex/30/"}},{"entry_number":103,"pokedex":{"name":"kitakami","url":"https://pokeapi.co/api/v2/pokedex/32/"}}],"shape":{"name":"humanoid","url":"https://pokeapi.co/api/v2/pokemon-shape/12/"},"varieties":[{"is_default":true,"pokemon":{"name":"snorlax","url":"https://pokeapi.co/api/v2/pokemon/143/"}},{"is_default":false,"pokemon":{"name":"snorlax-gmax","url":"https://pokeapi.co/api/v2/pokemon/10206/"}}]}

3. Welchen Python Datentyp liefert die Methoden .json() einer requests-Response zurück?

# Your Code

print(type(r.json()))

<class 'dict'>

Antwort: Der Type der response r ist dict. Das gleicht einem Python Dictionary.

4. Schauen Sie sich nochmal das Kapitel zum unüberwachten Lernen der KI-Vorlesung an: https://lectures.mi.hdm-stuttgart.de/mi7ai/06Clustering.html#introduction (user: mi7ai, pw: ailecture)
5. Beschreibt kurz die Clustering-Verfahren KMeans und DBSCAN. Was sind die jeweiligen Vor- und Nachteile der beiden Verfahren? Worin unterscheiden sie sich?

KMeans

Beschreibung:

Bei dem KMeans Clustering Verfahren werden anfangs zufällige Cluster Mitten ausgewählt. Die Trainingsdaten werden dem Cluster zugewiesen, wessen Mitte am nächsten ist. Für jedes Cluster wird die Mitte neu ermittelt in dem der Schwerpunkt von allen Trainigsdaten berechnet wird, welche zu einem Cluster zugeteilt wurden. Wenn sich die Mitten der Cluster nicht mehr verschieben ist das Clustering fertig.

Pros:

• Effizient und schnell, insbesondere bei großen Datensätzen
• Minimiert den Rekonstruktionsfehler, was den Algorithmus ideal für Quantisierungsaufgaben macht
• In der Regel wird die euklidische Distanz als Maß verwendet, aber es sind auch andere Distanz-Metriken möglich

Cons:

• Die Anzahl der Cluster muss vom Benutzer vorgegeben werden, was die Auswahl eines geeigneten Wertes von k erfordert
• Das Ergebnis kann stark von den zufällig gewählten Anfangs-Clustern abhängen, da der Algorithmus in lokalen Minima stecken bleiben kann
• K-Means ist anfällig für Ausreißer, die das Ergebnis verzerren können, da alle Punkte einem Cluster zugewiesen werden
• Er eignet sich nur für Cluster, die ungefähr kreisförmig und gleich groß sind. Nicht-lineare oder überlappende Clusterformen kann der Algorithmus schlecht abbilden

DBSCAN

Beschreibung:

Der DBSCAN ermöglicht es, Cluster in einem Datensatz zu identifizieren der nicht nur auf räumlicher Nähe, sondern auch auf Dichte basiert. Anders als bei K-Means benötigt DBSCAN keine im voraus festgelegten Cluster. Somit ist dieser Algorithmus flexibler als andere. Ein zufällig nicht besuchter Kernpunkt wird ausgewählt und alle erreichbaren Punkte in seiner Nachbarshaft werden identifiziert. Diese Punkt bilden ein Cluster. Dieser Vorgang wird nun wiederholt, indem er neue Kernpunkte findet, die nicht Teil eines Clusters sind, und die Nachbarschaft jedes Kernpunkts durchsucht, bis alle Kernpunkte besucht wurden.

Pros:

• Keine Anzahl an Clustern muss vorher festgelegt werden
• Ausreißer werden nicht den Clustern zugewiesen und beeinträchtigen nicht das Ergebnis
• Ist auch für größere Datensätze gut angepasst
• Skaliet auch bei großen Datensätzen gut

Cons:

• Es kann schwierig sein Ausreißer zu identifizieren und zu behandeln
• Es kann schwierig sein vorallem bei großen Datensätzen, geeignete Werte für die Parameter ε (maximale Distanz zweier Punkte um als benachbart zu gelten) und MinPts (minimal benötigte Anzahl von Punkte dass ein Punkt als Kernpunkt gilt) zu finden

Unterschiede:

Konfigurationparameter:

• K-means: Clusteranzahl
• DBSCAN: ε ,M mindest Anzahl der nahen Punkte um ein Kernpunkt zu sein

Beachten von Ausreisern:

• K-means: ja, ordnet diese auch zu clustern hinzu
• DBSCAN: nein, ignoriert diese und fügt sie keinem cluster hinzu

Durchführung

1. Data Collection, Feature Extraction & Preprocessing

Als Erstes müssen wir Daten über die API holen, damit wir diese für das Erstellen der Features benutzen können. Wir arbeiten hier nur mit Daten über Pokemons und nicht mit den verschiedenen Fertigkeiten oder Attacken. Diese können für weitere eigene Experimente benutzt werden. Diesmal installieren wir keine spezielle Bibliothek, sondern holen uns die Daten selber über die requests Bibliothek. Damit wollen wir euch zeigen, dass es auch ohne spezielle Frameworks sehr leicht ist, Daten in Python zu crawlen. Die meisten APIs arbeiten heutzutage mit JSON, es gibt aber auch noch Ausnahmen, die z.B. mit XML arbeiten. JSON-Objekte kann requests ohne Probleme selber parsen, da ein JSON-Objekt einfach in ein Python-Dictionary umgewandelt werden kannn.

Aufgaben 1:

1. Implementiert eine Funktion call_api(endpoint, resource_id), die euch für verschiedene endpoints Daten in einem Dictionary zurückgeben kann. Die endpoint URL muss dabei dynamisch um die id (oder den Namen) erweitert werden.
2. Um HTTP Fehler zu bemerken ruft auf dem Response-Objekt die Methode raise_for_status() auf damit wir mögliche Exceptions später korrekt abfangen können.
3. Zum Testen der Methode, holt euch die Daten für Pikachu über den Endpoint https://pokeapi.co/api/v2/pokemon/{id or name}/. Pikachu hat die id 25. Was passiert bei id 0? Analysiert die erste Ebene des Dictionaries mit der .keys() Methode.

def call_api(endpoint, resource_id):
    url = f"https://pokeapi.co/api/v2/{endpoint}/{resource_id}"
    r = req.get(url)
    
    rsponse_status = r.raise_for_status()
    return(r.json())

pika = call_api("pokemon", 25)
pika

{'abilities': [{'ability': {'name': 'static',
    'url': 'https://pokeapi.co/api/v2/ability/9/'},
   'is_hidden': False,
   'slot': 1},
  {'ability': {'name': 'lightning-rod',
    'url': 'https://pokeapi.co/api/v2/ability/31/'},
   'is_hidden': True,
   'slot': 3}],
 'base_experience': 112,
 'cries': {'latest': 'https://raw.githubusercontent.com/PokeAPI/cries/main/cries/pokemon/latest/25.ogg',
  'legacy': 'https://raw.githubusercontent.com/PokeAPI/cries/main/cries/pokemon/legacy/25.ogg'},
 'forms': [{'name': 'pikachu',
   'url': 'https://pokeapi.co/api/v2/pokemon-form/25/'}],
 'game_indices': [{'game_index': 84,
   'version': {'name': 'red', 'url': 'https://pokeapi.co/api/v2/version/1/'}},
  {'game_index': 84,
   'version': {'name': 'blue', 'url': 'https://pokeapi.co/api/v2/version/2/'}},
  {'game_index': 84,
   'version': {'name': 'yellow',
    'url': 'https://pokeapi.co/api/v2/version/3/'}},
  {'game_index': 25,
   'version': {'name': 'gold', 'url': 'https://pokeapi.co/api/v2/version/4/'}},
  {'game_index': 25,
   'version': {'name': 'silver',
    'url': 'https://pokeapi.co/api/v2/version/5/'}},
  {'game_index': 25,
   'version': {'name': 'crystal',
    'url': 'https://pokeapi.co/api/v2/version/6/'}},
  {'game_index': 25,
   'version': {'name': 'ruby', 'url': 'https://pokeapi.co/api/v2/version/7/'}},
  {'game_index': 25,
   'version': {'name': 'sapphire',
    'url': 'https://pokeapi.co/api/v2/version/8/'}},
  {'game_index': 25,
   'version': {'name': 'emerald',
    'url': 'https://pokeapi.co/api/v2/version/9/'}},
  {'game_index': 25,
   'version': {'name': 'firered',
    'url': 'https://pokeapi.co/api/v2/version/10/'}},
  {'game_index': 25,
   'version': {'name': 'leafgreen',
    'url': 'https://pokeapi.co/api/v2/version/11/'}},
  {'game_index': 25,
   'version': {'name': 'diamond',
    'url': 'https://pokeapi.co/api/v2/version/12/'}},
  {'game_index': 25,
   'version': {'name': 'pearl',
    'url': 'https://pokeapi.co/api/v2/version/13/'}},
  {'game_index': 25,
   'version': {'name': 'platinum',
    'url': 'https://pokeapi.co/api/v2/version/14/'}},
  {'game_index': 25,
   'version': {'name': 'heartgold',
    'url': 'https://pokeapi.co/api/v2/version/15/'}},
  {'game_index': 25,
   'version': {'name': 'soulsilver',
    'url': 'https://pokeapi.co/api/v2/version/16/'}},
  {'game_index': 25,
   'version': {'name': 'black',
    'url': 'https://pokeapi.co/api/v2/version/17/'}},
  {'game_index': 25,
   'version': {'name': 'white',
    'url': 'https://pokeapi.co/api/v2/version/18/'}},
  {'game_index': 25,
   'version': {'name': 'black-2',
    'url': 'https://pokeapi.co/api/v2/version/21/'}},
  {'game_index': 25,
   'version': {'name': 'white-2',
    'url': 'https://pokeapi.co/api/v2/version/22/'}}],
 'height': 4,
 'held_items': [{'item': {'name': 'oran-berry',
    'url': 'https://pokeapi.co/api/v2/item/132/'},
   'version_details': [{'rarity': 50,
     'version': {'name': 'ruby',
      'url': 'https://pokeapi.co/api/v2/version/7/'}},
    {'rarity': 50,
     'version': {'name': 'sapphire',
      'url': 'https://pokeapi.co/api/v2/version/8/'}},
    {'rarity': 50,
     'version': {'name': 'emerald',
      'url': 'https://pokeapi.co/api/v2/version/9/'}},
    {'rarity': 50,
     'version': {'name': 'diamond',
      'url': 'https://pokeapi.co/api/v2/version/12/'}},
    {'rarity': 50,
     'version': {'name': 'pearl',
      'url': 'https://pokeapi.co/api/v2/version/13/'}},
    {'rarity': 50,
     'version': {'name': 'platinum',
      'url': 'https://pokeapi.co/api/v2/version/14/'}},
    {'rarity': 50,
     'version': {'name': 'heartgold',
      'url': 'https://pokeapi.co/api/v2/version/15/'}},
    {'rarity': 50,
     'version': {'name': 'soulsilver',
      'url': 'https://pokeapi.co/api/v2/version/16/'}},
    {'rarity': 50,
     'version': {'name': 'black',
      'url': 'https://pokeapi.co/api/v2/version/17/'}},
    {'rarity': 50,
     'version': {'name': 'white',
      'url': 'https://pokeapi.co/api/v2/version/18/'}}]},
  {'item': {'name': 'light-ball',
    'url': 'https://pokeapi.co/api/v2/item/213/'},
   'version_details': [{'rarity': 5,
     'version': {'name': 'ruby',
      'url': 'https://pokeapi.co/api/v2/version/7/'}},
    {'rarity': 5,
     'version': {'name': 'sapphire',
      'url': 'https://pokeapi.co/api/v2/version/8/'}},
    {'rarity': 5,
     'version': {'name': 'emerald',
      'url': 'https://pokeapi.co/api/v2/version/9/'}},
    {'rarity': 5,
     'version': {'name': 'diamond',
      'url': 'https://pokeapi.co/api/v2/version/12/'}},
    {'rarity': 5,
     'version': {'name': 'pearl',
      'url': 'https://pokeapi.co/api/v2/version/13/'}},
    {'rarity': 5,
     'version': {'name': 'platinum',
      'url': 'https://pokeapi.co/api/v2/version/14/'}},
    {'rarity': 5,
     'version': {'name': 'heartgold',
      'url': 'https://pokeapi.co/api/v2/version/15/'}},
    {'rarity': 5,
     'version': {'name': 'soulsilver',
      'url': 'https://pokeapi.co/api/v2/version/16/'}},
    {'rarity': 1,
     'version': {'name': 'black',
      'url': 'https://pokeapi.co/api/v2/version/17/'}},
    {'rarity': 1,
     'version': {'name': 'white',
      'url': 'https://pokeapi.co/api/v2/version/18/'}},
    {'rarity': 5,
     'version': {'name': 'black-2',
      'url': 'https://pokeapi.co/api/v2/version/21/'}},
    {'rarity': 5,
     'version': {'name': 'white-2',
      'url': 'https://pokeapi.co/api/v2/version/22/'}},
    {'rarity': 5,
     'version': {'name': 'x', 'url': 'https://pokeapi.co/api/v2/version/23/'}},
    {'rarity': 5,
     'version': {'name': 'y', 'url': 'https://pokeapi.co/api/v2/version/24/'}},
    {'rarity': 5,
     'version': {'name': 'omega-ruby',
      'url': 'https://pokeapi.co/api/v2/version/25/'}},
    {'rarity': 5,
     'version': {'name': 'alpha-sapphire',
      'url': 'https://pokeapi.co/api/v2/version/26/'}},
    {'rarity': 5,
     'version': {'name': 'sun',
      'url': 'https://pokeapi.co/api/v2/version/27/'}},
    {'rarity': 5,
     'version': {'name': 'moon',
      'url': 'https://pokeapi.co/api/v2/version/28/'}},
    {'rarity': 5,
     'version': {'name': 'ultra-sun',
      'url': 'https://pokeapi.co/api/v2/version/29/'}},
    {'rarity': 5,
     'version': {'name': 'ultra-moon',
      'url': 'https://pokeapi.co/api/v2/version/30/'}}]}],
 'id': 25,
 'is_default': True,
 'location_area_encounters': 'https://pokeapi.co/api/v2/pokemon/25/encounters',
 'moves': [{'move': {'name': 'mega-punch',
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    'gold': {'back_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-ii/gold/back/25.png',
     'back_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-ii/gold/back/shiny/25.png',
     'front_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-ii/gold/25.png',
     'front_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-ii/gold/shiny/25.png',
     'front_transparent': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-ii/gold/transparent/25.png'},
    'silver': {'back_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-ii/silver/back/25.png',
     'back_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-ii/silver/back/shiny/25.png',
     'front_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-ii/silver/25.png',
     'front_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-ii/silver/shiny/25.png',
     'front_transparent': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-ii/silver/transparent/25.png'}},
   'generation-iii': {'emerald': {'front_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iii/emerald/25.png',
     'front_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iii/emerald/shiny/25.png'},
    'firered-leafgreen': {'back_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iii/firered-leafgreen/back/25.png',
     'back_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iii/firered-leafgreen/back/shiny/25.png',
     'front_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iii/firered-leafgreen/25.png',
     'front_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iii/firered-leafgreen/shiny/25.png'},
    'ruby-sapphire': {'back_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iii/ruby-sapphire/back/25.png',
     'back_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iii/ruby-sapphire/back/shiny/25.png',
     'front_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iii/ruby-sapphire/25.png',
     'front_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iii/ruby-sapphire/shiny/25.png'}},
   'generation-iv': {'diamond-pearl': {'back_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/diamond-pearl/back/25.png',
     'back_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/diamond-pearl/back/female/25.png',
     'back_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/diamond-pearl/back/shiny/25.png',
     'back_shiny_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/diamond-pearl/back/shiny/female/25.png',
     'front_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/diamond-pearl/25.png',
     'front_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/diamond-pearl/female/25.png',
     'front_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/diamond-pearl/shiny/25.png',
     'front_shiny_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/diamond-pearl/shiny/female/25.png'},
    'heartgold-soulsilver': {'back_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/heartgold-soulsilver/back/25.png',
     'back_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/heartgold-soulsilver/back/female/25.png',
     'back_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/heartgold-soulsilver/back/shiny/25.png',
     'back_shiny_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/heartgold-soulsilver/back/shiny/female/25.png',
     'front_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/heartgold-soulsilver/25.png',
     'front_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/heartgold-soulsilver/female/25.png',
     'front_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/heartgold-soulsilver/shiny/25.png',
     'front_shiny_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/heartgold-soulsilver/shiny/female/25.png'},
    'platinum': {'back_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/platinum/back/25.png',
     'back_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/platinum/back/female/25.png',
     'back_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/platinum/back/shiny/25.png',
     'back_shiny_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/platinum/back/shiny/female/25.png',
     'front_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/platinum/25.png',
     'front_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/platinum/female/25.png',
     'front_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/platinum/shiny/25.png',
     'front_shiny_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-iv/platinum/shiny/female/25.png'}},
   'generation-v': {'black-white': {'animated': {'back_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-v/black-white/animated/back/25.gif',
      'back_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-v/black-white/animated/back/female/25.gif',
      'back_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-v/black-white/animated/back/shiny/25.gif',
      'back_shiny_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-v/black-white/animated/back/shiny/female/25.gif',
      'front_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-v/black-white/animated/25.gif',
      'front_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-v/black-white/animated/female/25.gif',
      'front_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-v/black-white/animated/shiny/25.gif',
      'front_shiny_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-v/black-white/animated/shiny/female/25.gif'},
     'back_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-v/black-white/back/25.png',
     'back_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-v/black-white/back/female/25.png',
     'back_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-v/black-white/back/shiny/25.png',
     'back_shiny_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-v/black-white/back/shiny/female/25.png',
     'front_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-v/black-white/25.png',
     'front_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-v/black-white/female/25.png',
     'front_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-v/black-white/shiny/25.png',
     'front_shiny_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-v/black-white/shiny/female/25.png'}},
   'generation-vi': {'omegaruby-alphasapphire': {'front_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-vi/omegaruby-alphasapphire/25.png',
     'front_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-vi/omegaruby-alphasapphire/female/25.png',
     'front_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-vi/omegaruby-alphasapphire/shiny/25.png',
     'front_shiny_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-vi/omegaruby-alphasapphire/shiny/female/25.png'},
    'x-y': {'front_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-vi/x-y/25.png',
     'front_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-vi/x-y/female/25.png',
     'front_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-vi/x-y/shiny/25.png',
     'front_shiny_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-vi/x-y/shiny/female/25.png'}},
   'generation-vii': {'icons': {'front_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-vii/icons/25.png',
     'front_female': None},
    'ultra-sun-ultra-moon': {'front_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-vii/ultra-sun-ultra-moon/25.png',
     'front_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-vii/ultra-sun-ultra-moon/female/25.png',
     'front_shiny': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-vii/ultra-sun-ultra-moon/shiny/25.png',
     'front_shiny_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-vii/ultra-sun-ultra-moon/shiny/female/25.png'}},
   'generation-viii': {'icons': {'front_default': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-viii/icons/25.png',
     'front_female': 'https://raw.githubusercontent.com/PokeAPI/sprites/master/sprites/pokemon/versions/generation-viii/icons/female/25.png'}}}},
 'stats': [{'base_stat': 35,
   'effort': 0,
   'stat': {'name': 'hp', 'url': 'https://pokeapi.co/api/v2/stat/1/'}},
  {'base_stat': 55,
   'effort': 0,
   'stat': {'name': 'attack', 'url': 'https://pokeapi.co/api/v2/stat/2/'}},
  {'base_stat': 40,
   'effort': 0,
   'stat': {'name': 'defense', 'url': 'https://pokeapi.co/api/v2/stat/3/'}},
  {'base_stat': 50,
   'effort': 0,
   'stat': {'name': 'special-attack',
    'url': 'https://pokeapi.co/api/v2/stat/4/'}},
  {'base_stat': 50,
   'effort': 0,
   'stat': {'name': 'special-defense',
    'url': 'https://pokeapi.co/api/v2/stat/5/'}},
  {'base_stat': 90,
   'effort': 2,
   'stat': {'name': 'speed', 'url': 'https://pokeapi.co/api/v2/stat/6/'}}],
 'types': [{'slot': 1,
   'type': {'name': 'electric', 'url': 'https://pokeapi.co/api/v2/type/13/'}}],
 'weight': 60}

#call_api("pokemon", 0) #results in Error Code 404

Antwort: Bei id=0 bekommen wir einen 404 Error, da kein Eintrag mit der id=0 existiert.

print(call_api("pokemon", 25).keys())

dict_keys(['abilities', 'base_experience', 'cries', 'forms', 'game_indices', 'height', 'held_items', 'id', 'is_default', 'location_area_encounters', 'moves', 'name', 'order', 'past_abilities', 'past_types', 'species', 'sprites', 'stats', 'types', 'weight'])

Antwort: Durch das Aufrufen der .keys() Funktion, bekommen wir eine Liste an features, welche in der API vorhanden und abrufbar sind.

4. Analysiert die Struktur bzw. die Datentypen der Keys name, height, weight sowie stats und types aus dem Dictionary mit der Beschreibung der API Dokumentation.

result = call_api(endpoint='pokemon', resource_id=25)

print(f'''{result['id']}: {result['name']}
name:   {type(result["name"])}  # The name for this resource.
height: {type(result["height"])}  # The height of this Pokémon in decimetres.
weight: {type(result["weight"])}  # The weight of this Pokémon in hectograms.
stats:  {type(result['stats'])} # A list of base stat values for this Pokémon.
types:  {type(result['types'])} # A list of details showing types this Pokémon has.
''')

25: pikachu
name:   <class 'str'>  # The name for this resource.
height: <class 'int'>  # The height of this Pokémon in decimetres.
weight: <class 'int'>  # The weight of this Pokémon in hectograms.
stats:  <class 'list'> # A list of base stat values for this Pokémon.
types:  <class 'list'> # A list of details showing types this Pokémon has.

5. Gebt für Pikachu nun manuell den Name, Höhe und Gewicht, die Namen und Werte der Stats sowie die Typ(en) aus. Für Stats und Types ist eine geschachtelte Abfrage notwendig. Beachten Sie, dass manche Pokemons mehr als einen Typ haben können.

result = call_api(endpoint='pokemon', resource_id=25)

print(f'''--- PIKACHU ---
name:   {result["name"]}
height: {result["height"]}
weight: {result["weight"]}''')

print('--- Stats: ---')
for i in result['stats']:
    print(i['stat']['name'], ':', i['base_stat'])
    
print('--- Types: ---')
for i in result['types']:
    print('Type', ':', i['type']['name'])

--- PIKACHU ---
name:   pikachu
height: 4
weight: 60
--- Stats: ---
hp : 35
attack : 55
defense : 40
special-attack : 50
special-defense : 50
speed : 90
--- Types: ---
Type : electric

Aufgabe 2:

1. Implementiert nun eine Funktion parse_poke_dict(pokemon), die das Dictionary, das von der call_api-Funktion zurückgegeben wurde, in ein Dictionary mit ausgewählten Features umwandelt (reduziert). Verwendet dafür die Keys und Features aus der vorherigen Aufgabe.
2. Das neue Dictionary soll nicht mehr verschachtelt sein. Einzelne Stats werden also auf der selben Hierarchieebene wie name, height und weight eingefügt. Verfügbare Types werden als Keys verwendet und auf den Value 1 gesetzt. Das ermöglicht uns später eine einfache Umwandlung der Types in ein binäres Encoding mit Pandas (ähnlich zu One-Hot Encoding, ein Pokemon kann aber mehr als einen Type haben).

# Your Code
def parse_poke_dict(pokemon):
    feature_list = ['name', 'height', 'weight', 'stats', 'types']

    selected_features = {key:pokemon[key] for key in pokemon if key in feature_list}

    stats = selected_features['stats']
    for i in stats:
        selected_features[i['stat']['name']] = i['base_stat']

    types = selected_features['types']
    for j in types:
        selected_features[j['type']['name']] = 1

    selected_features.pop('stats', None)
    selected_features.pop('types', None)

    return selected_features

3. Vergleichen Sie die neue Methode mit der manuellen Abfrage um sicherzustellen, dass alle Features korrekt übernommen wurden.

pika = parse_poke_dict(call_api('pokemon', 25))
pika

{'height': 4,
 'name': 'pikachu',
 'weight': 60,
 'hp': 35,
 'attack': 55,
 'defense': 40,
 'special-attack': 50,
 'special-defense': 50,
 'speed': 90,
 'electric': 1}

Aufgabe 3:

1. Implementiert eine Funktion get_img(pokemon) welche ein noch nicht reduziertes Pokemon Dictionary akzeptiert und für dieses Pokemon das entsprechende Sprite (Bild) herunterlädt und zurück gibt. Hierzu ist ein weiterer get-Request notwendig.
2. Die Download URLs können über den Key 'sprites' abgfragt werden. Wir möchten die Ansicht 'front_default' herunterladen.
3. Der Inhalt der Response kann mit .content abgerufen werden. Um ein RGB-Bild zu erhalten muss dieser zunächst der Methode BytesIO und dann der Methode Image.open übergeben werden. Hierzu ist die Pillow-Bibliothek notwendig. Installiert diese also zunächst über pip und importiert dann das Image-Modul mit: from PIL import Image. Die BytesIO-Methode kann über die Python standard Bibliothek io importiert werden. Welchen Datentyp haben die Bilder die von der Methode zurückgegeben werden?

# Your Code
def get_img(pokemon):
    url = pokemon['sprites']['front_default']
    picture = req.get(url)
    return Image.open(BytesIO(picture.content))

type(get_img(call_api('pokemon',25)))

PIL.PngImagePlugin.PngImageFile

Antwort: Der Datentyp, welchen die get_img() FUnktion zurückgibt ist ein PngImageFile.

4. Für die Ausgabe der Bilder könnt ihr anstatt print die display Methode verwenden. Jupyter ruft diese implizit immer auf dem letzten Rückgabewert einer Zelle auf, daher werden Bilder angezeigt wenn sie der letzte Rückgabewert der Zelle waren.

display(get_img(call_api('pokemon',25)))

Aufgabe 4:

Mit den bisher implementierten Methoden können wir nun unseren Pokedex erstellen. Dieser könnte zwar als Klasse implementiert werden, wir speichern die Daten und Bilder jedoch der Einfachheit halber separat in einem Pandas Dataframe und einem Numpy Array.

1. Implementiert eine Funktion catch_them_all(n=151), welche für $n$ Pokemon die reduzierten Feature Dictionaries und Bilder sammelt. Wir verwenden $n=151$ als Default da das alle Pokemon aus der ersten Edition sind.
2. Verwendet die Methoden call_api, parse_poke_dict und get_img in einer Schleife um alle Pokemon abzufragen.
3. Die Dictionaries sollen zunächst in einer Python Liste gesammelt, und dann als Pandas Dataframe ausgegeben werden. Als Index soll dabei name verwendet werden. Was fällt bei der Betrachtung der kategorialen Einträge auf? Behebt das Problem in dem Ihr einen passenden Wert für fehlende Einträge setzt.
4. Die Bilder sammeln wir separat in einem Numpy Array. Da Numpy etwas picky sein kann wenn Objekte wie PNG Bilder abgespeichert werden sollen, erstellen wir die Listen nicht wie üblich mit np.array([]) sondern mit np.empty(shape=(n,), dtype=object). Um Objekte hinzuzufügen wird dann nicht wie üblich np.append verwendet sondern eine Zuweisung mit Index, also a[i] = item.
5. Der Grund warum wir hier ein Numpy array verwenden ist, dass wir später potentielle Cluster sehr einfach über eine Indize-Liste abfragen können, sowohl im Dataframe als auch im Array. Beispiel:

demo_data = pd.DataFrame(['Poke-1', 'Poke-2', 'Poke-3'])
demo_imgs = np.array(    ['IMG-1' , 'IMG-2' , 'IMG-3' ])

cluster_indices = [2,0]

display(demo_data.iloc[cluster_indices])
display(demo_imgs[cluster_indices])

	0
2	Poke-3
0	Poke-1

array(['IMG-3', 'IMG-1'], dtype='<U5')

def catch_them_all(n):
    pokemon_list = []
    pokemon_pictures = np.empty(shape=(n,), dtype=object)
    for i in tqdm(range(1,n+1)):
        try :
            pokemon = call_api('pokemon', i)
            picture = get_img(pokemon)
            reduced = parse_poke_dict(pokemon)
            pokemon_list.append(reduced)
            pokemon_pictures[i-1] = picture
        except ConnectionError as ce:
            print(f'Pokmon ID: {i}')
            print(f'{ce}: raise on the event of a network problem')
        except HTTPError as he:
            print(f'Pokmon ID: {i}')
            print(f'{he}: raise in the HTTP request returned an unsuccessful status code')
        except Timeout as te:
            print(f'Pokmon ID: {i}')
            print(f'{te}: raise if a request times out')
        except TooManyRedirects as tmre:
            print(f'Pokmon ID: {i}')
            print(f'{tmre}: raise if the request exceeds the confidgured number of maximum redirections')
 
    print(len(pokemon_list))
    pokemon_df = pd.DataFrame(pokemon_list)
    pokemon_df = pokemon_df.fillna(0.0)
    pokemon_df = pokemon_df.set_index('name')
    
    return pokemon_df, pokemon_pictures

Problem: Bei den Types werden für alle Types, über die ein Pokemon nicht verfügt, ein NaN eingefügt. Daraus folgen Fehler bei der späteren Verarbeitung. Diese NaNs müssen durch 0 ersetzt werden, um ein One-Hot-Encoding Format zu bekommen.

pokemons,pictures = catch_them_all(151)

100%|██████████| 151/151 [00:44<00:00,  3.41it/s]

151

pokemons.head()

	height	weight	hp	attack	defense	special-attack	special-defense	speed	grass	poison	fire	flying	water	bug	normal	electric	ground	fairy	fighting	psychic	rock	steel	ice	ghost	dragon
name
bulbasaur	7	69	45	49	49	65	65	45	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
ivysaur	10	130	60	62	63	80	80	60	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
venusaur	20	1000	80	82	83	100	100	80	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
charmander	6	85	39	52	43	60	50	65	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
charmeleon	11	190	58	64	58	80	65	80	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

pictures[:10]

array([<PIL.PngImagePlugin.PngImageFile image mode=P size=96x96 at 0x212D27EAA10>,
       <PIL.PngImagePlugin.PngImageFile image mode=P size=96x96 at 0x212DAFAEAD0>,
       <PIL.PngImagePlugin.PngImageFile image mode=P size=96x96 at 0x212DAAD4520>,
       <PIL.PngImagePlugin.PngImageFile image mode=P size=96x96 at 0x212D180DF60>,
       <PIL.PngImagePlugin.PngImageFile image mode=P size=96x96 at 0x212DAAD68F0>,
       <PIL.PngImagePlugin.PngImageFile image mode=P size=96x96 at 0x212DAAD7310>,
       <PIL.PngImagePlugin.PngImageFile image mode=P size=96x96 at 0x212DA6BC220>,
       <PIL.PngImagePlugin.PngImageFile image mode=P size=96x96 at 0x212D15D0F70>,
       <PIL.PngImagePlugin.PngImageFile image mode=P size=96x96 at 0x212D15D2FB0>,
       <PIL.PngImagePlugin.PngImageFile image mode=P size=96x96 at 0x212D15D1600>],
      dtype=object)

4. Um eine Fortschrittsanzeige während des Downloads zu erhalten, installiert zunächst das Paket tqdm, importiert es dann wie folgt: from tqdm.notebook import tqdm und verwendet es in eurer Schleife, z.B. mit: for i in tqdm(range(0, n)):. Evtl. müssen noch die ipywidgets installiert werden.
5. Welche Errors and Exceptions können bei der Verwendung der requests Bibliothek auftreten? Listet diese in einer separaten Markdownzelle auf mit einer kurzen Erklärung der Fehler.

Errors and Exceptions

• Connection Error: falls ein Netzwerkfehler auftritt.
• HTTPError: falls die response.raise_for_status() Methode einen unerfolgreichen Status code zurück sendet.
• Timeout: Exception im Falle eines Timeout bei einer Anfrage.
• TooManyRedirects: Exception, wenn bei einer Anfrage eine vorgegebene Anzahl an Weiterleitungen überstritten wird.

6. Wie kann sichergestellt werden, dass die Schleife bei einem Fehler nicht abbricht? Implementiert eine geeignete Lösung damit die Schleife auch mit requests-Exceptions weiterläuft. Informiert die Benutzer*innen der Methode bei welcher ID ein Fehler aufgetreten ist und gebt zusätzlich die Exception bzw. die Info, die der Error liefert. Tipp zum Testen: In Python können Exceptions manuell getriggert werden, z.B. mit raise KeyError('Hallo, das ist ein KeyError'), spezifische Exceptions einer Bibliothek müssen davor allerdings importiert werden. Deaktiviert alle manuell ausgelösten Test-Exceptions wieder soblad ihr euch sicher seid, dass diese robust abgefangen werden.
7. Testet euren Pokedex in dem ihr die Indizelisten [0,1,2] und [24,150] ausgeben lasst und stellt sicher, dass die Zuordnung zwischen Dataframe und Bild Array passt. Die display-Methode eignet sich hier wieder für die Ausgabe.

display(pokemons.iloc[[0,1,2]], pictures[0], pictures[1], pictures[2])

display(pokemons.iloc[[24,150]],pictures[24],pictures[150])

	height	weight	hp	attack	defense	special-attack	special-defense	speed	grass	poison	fire	flying	water	bug	normal	electric	ground	fairy	fighting	psychic	rock	steel	ice	ghost	dragon
name
bulbasaur	7	69	45	49	49	65	65	45	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
ivysaur	10	130	60	62	63	80	80	60	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
venusaur	20	1000	80	82	83	100	100	80	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	height	weight	hp	attack	defense	special-attack	special-defense	speed	grass	poison	fire	flying	water	bug	normal	electric	ground	fairy	fighting	psychic	rock	steel	ice	ghost	dragon
name
pikachu	4	60	35	55	40	50	50	90	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
mew	4	40	100	100	100	100	100	100	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0

Nachdem wir nun unser Dataset gesammelt haben sollten wir als Teil des Preprocessing noch sicherstellen, dass folgende Eigenschaften erfüllt sind:

1. Keine Null- oder NaN-Werte im DataFrame.
2. Binäres Encoding der kategorischen Werte.
3. Skalieren der Werte mit einem MinMaxScaler.

Zwei davon haben wir schon als Teil der Feature Extraction erledigt. Um die Skalierung kümmern wir uns jetzt.

Aufgabe 5:

1. Instanziiert ein from sklearn.preprocessing import MinMaxScaler Objekt und wendet diesen auf den Dataframe an.

max_scaler = MinMaxScaler()
data = pokemons
max_scaler.fit(data)
scaled_data = max_scaler.transform(data)

2. Die transformierten Daten sollen wieder in ein Pandas Dataframe umgewandelt werden. Die Columns sowie der Index sind dabei die Selben wie im unskalierten Dataframe.

scaled_df_pokemon = pd.DataFrame(scaled_data, index=data.index, columns=data.columns)

3. Gebt die originalen und skalierten Daten aus, um die Dataframes zu vergleichen.

display(scaled_df_pokemon, pokemons)

	height	weight	hp	attack	defense	special-attack	special-defense	speed	grass	poison	fire	flying	water	bug	normal	electric	ground	fairy	fighting	psychic	rock	steel	ice	ghost	dragon
name
bulbasaur	0.058140	0.014786	0.145833	0.341085	0.251429	0.359712	0.428571	0.222222	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
ivysaur	0.093023	0.028050	0.208333	0.441860	0.331429	0.467626	0.571429	0.333333	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
venusaur	0.209302	0.217221	0.291667	0.596899	0.445714	0.611511	0.761905	0.481481	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
charmander	0.046512	0.018265	0.120833	0.364341	0.217143	0.323741	0.285714	0.370370	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
charmeleon	0.104651	0.041096	0.200000	0.457364	0.302857	0.467626	0.428571	0.481481	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
dratini	0.186047	0.006958	0.129167	0.457364	0.228571	0.251799	0.285714	0.259259	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
dragonair	0.441860	0.035660	0.212500	0.612403	0.342857	0.395683	0.476190	0.407407	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
dragonite	0.232558	0.456404	0.337500	1.000000	0.514286	0.611511	0.761905	0.481481	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
mewtwo	0.209302	0.265058	0.400000	0.813953	0.485714	1.000000	0.666667	0.851852	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0
mew	0.023256	0.008480	0.375000	0.736434	0.542857	0.611511	0.761905	0.629630	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0

151 rows × 25 columns

	height	weight	hp	attack	defense	special-attack	special-defense	speed	grass	poison	fire	flying	water	bug	normal	electric	ground	fairy	fighting	psychic	rock	steel	ice	ghost	dragon
name
bulbasaur	7	69	45	49	49	65	65	45	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
ivysaur	10	130	60	62	63	80	80	60	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
venusaur	20	1000	80	82	83	100	100	80	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
charmander	6	85	39	52	43	60	50	65	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
charmeleon	11	190	58	64	58	80	65	80	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
dratini	18	33	41	64	45	50	50	50	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
dragonair	40	165	61	84	65	70	70	70	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
dragonite	22	2100	91	134	95	100	100	80	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0
mewtwo	20	1220	106	110	90	154	90	130	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0
mew	4	40	100	100	100	100	100	100	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0

151 rows × 25 columns

4. Importiert abschliesend die Bibliothek seaborn mit import seaborn as sns und analysiert die skalierten numerischen Daten mit einem sns.pairplot (link). Wählt dazu nur die numerischen Columns des Dataframes aus.

sns.pairplot(scaled_df_pokemon.iloc[:, :8])

<seaborn.axisgrid.PairGrid at 0x212df4acbe0>

5. Beschreibt kurz Auffälligkeiten in den Verteilungen (falls vorhanden).

• Die Kampf-irrelevanten Eigenschaften sind jeweils links verteilt (height, weight). Eine Außnahme hat der Stat hp. (Wahrscheinlich damit die Kämpfe nicht zu lange gehen)
• Die Kampf-relevanten Eigenschaften sind alle mitting verteilt, bis auf ein paar Ausreißer (sehr schwache Pokemon vs. Legendary Pokemon?)
• weight und height korrelieren sehr stark --> kleine Pokemon sind meist auch leicht, große sind schwer
• Die meisten Pokemon finden sich auf der linken Seite der Verteilung: Die meisten Pokemon sind klein (so gut wie alle größeren Pokemons haben eine/zwei kleinere Vorentwicklungen, aber nicht jedes kleine Pokemon hat eine große Weiterentwicklung)
• Die Kampf-relevanten Eigenschaften sind in Bezug auf andere sehr weit verteilt im ganzen Scatterplot
• Es gibt nur sehr wenige Pokemon mit besonders hohen Werten in allen Kampf-relevanten Bereichen (Mewto & Mew?)

2. Data Visualization & Dimensionsreduktion

Konntet ihr bereits Cluster in den Daten ausfindig machen? Nein? Keine Sorge, wir auch nicht.

In diesem Abschnitt verwenden wir daher die Principal Component Analysis (PCA) Methode um unsere hochdimensionalen Daten in einem 2D-Raum abzubilden. Diese Methode wird in einer späteren Übung noch genauer erklärt und benutzt. Hier soll sie einfach als Blackbox verwendet werden. Man sollte nur wissen, dass damit ein hochdimensionaler Raum auf wenige Dimensionen reduziert werden kann ohne dabei viel Information zu verlieren. Auch das Clustering werden wir in diesem reduzierten Raum durchführen. Bevor wir uns aber um die PCA kümmern implementieren wir noch ein paar hilfreiche Visualisierungsfunktionen mit denen wir später die gefundenen Cluster besser analysieren können.

Aufgabe 6:

1. Implementiert eine Funktion plot_images(img_list) die als Input ein Numpy Array mit Pillow Image Objekten akzeptiert und diese in einem fixen Grid visualisiert. Verwendet dazu die Bibliothek matplotlib mit import matplotlib.pyplot as plt.
2. Erstellt zunächst eine neue Figure mit einer geeigneten quadratischen figsize. Pillow Images können mit der Funktion imshow geplottet werden. Um die Plots in einm Grid anzuordnen kann die Funktion add_subplot(nrows, ncols, index) verwendet werden (index muss bei 1 starten), bei 151 Pokemon ist ein 13x13 Grid eine gute Wahl. Für jeden Subplot können die Achsen mit plt.axis('off') deaktiviert werden.
3. Um eine Liste zu iterieren und dabei auch den aktuellen Index zu erhalten kann die Funktion enumerate(['a','b','c']) verwendet werden (Achtung: Index startet bei 0).
4. Nachdem alle Subplots erstellt wurden, muss einmal plt.show() aufgerufen werden.

def plot_images(img_list):
    x= math.ceil(math.sqrt(len(img_list)))
    fig = plt.figure(figsize =(x,x))
    img_list_enu = enumerate(img_list)
    for index, value in img_list_enu:
        fig.add_subplot(x,x, index+1)
        plt.axis('off')
        plt.imshow(value)
    plt.show()

5. Testet die Funktion mit dem Indize Subset [0,1,2,24] und einmal mit allen Pokemon.

# Test Cell
plot_images(pictures[[0,1,2,24]])
plot_images(pictures)

Aufgabe 7:

1. Implementiert zwei Funktionen plot_numerics(dataframe) und plot_categories(dataframe) die als Input ein komplettes Dataframe akzeptieren aber jeweils nur die numerischen oder kategorischen Spalten visualisieren. Verwendet dazu wieder die Bibliothek seaborn.
2. Die Methoden sollen selbständig nur die numerischen oder kategorischen Spalten des Dataframes auswählen.
3. Für numerische Werte könnt ihr entweder ein sns.barplot (link) oder sns.boxplot (link) verwenden. Damit wir später verschiedene Cluster miteinander vergleichen können ist eine konstante Y-Range sinnvoll, z.B. plt.ylim(0,1.1).
4. Für kategorische Werte verwendet ihr ein sns.barplot. Per Default wendet seaborn hier den estimator='mean' auf die Daten an. Ist das für die kategorischen Columns eine geeignete Statistik? Mit welcher einfachen arithmetischen Funktion könnt ihr die Anzahl der binär codierten Kategorien zählen? Verwendet diese als estimator und wählt eine passende konstante Y-Range. Falls Fehlerbalken keine Sinn machen, können diese mit errorbar=None deaktiviert werden.
5. Die Labels der X-Achse können mit plt.xticks(rotation=90) rotiert werden.
6. Ruft in den Funktionen noch nicht plt.show() auf damit wir diese in einer Grid Darstellung verwenden können.

def plot_numerics(dataframe):
    data = dataframe.iloc[:,:8]
    plt.ylim(-0.1,1.1)
    sns.boxplot(data)

def plot_categories(dataframe):
    data = dataframe.iloc[:,8:]
    plt.ylim(0,35.0) # wir haben uns für 35 entschieden da später einige Typen einen max count von 30 haben
    sns.barplot(data, estimator='sum', errorbar=None) # Bei dem estimator haben wir uns für sum entschieden, da wir die Anzahl der Typen pro CLuster zählen möchten

7. Testet eure Funktionen mit dem skalierten Dataframe.

plot_numerics(scaled_df_pokemon)
print('Numeric Values Boxplot')
plt.xticks(rotation=90)
plt.show()

plot_categories(scaled_df_pokemon)
print("Categoric Values Barplot")
plt.xticks(rotation=90)
plt.show()

Numeric Values Boxplot

Categoric Values Barplot

Die drei bisher implementierten Funktionen möchten wir nun dazu verwenden, um gefundene Cluster zu visualiseren. Mit der untenstehenden Funktion könnt Ihr euch zum Test Fake Cluster generieren. Später implementieren wir diese Datenstruktur mit den echten Clustern. Die Keys des Dictionaries sind die Cluster Namen, die Values jeweils eine Liste mit den Indizes der Pokemon, die zu diesem Cluster gehören.

def get_fake_cluster(n_cluster=5, items_per_cluster=10, n_pokemon=151):

    cluster_dict = {}
    fake_cluster = np.random.choice(n_pokemon, (n_cluster, items_per_cluster), replace=False)
    for i, c in enumerate(fake_cluster):
        cluster_dict[i] = c
    return cluster_dict

test_cluster = get_fake_cluster()
test_cluster

{0: array([ 92, 107, 113, 116, 101,  22,  15,  53,  79,  61], dtype=int32),
 1: array([ 99, 137,  11,  34,   5,  56,  73,  66,  14,  57], dtype=int32),
 2: array([ 72, 125, 139,  64,  51, 110, 149,  69,  80,  84], dtype=int32),
 3: array([ 83,  63, 106, 132, 123,  26, 144,  87,   8,  37], dtype=int32),
 4: array([ 25,  40,  29,  62,  10, 135, 148,  82,  43,  36], dtype=int32)}

Aufgabe 8:

1. Implementiert eine Funktion compare_cluster(cluster_dict, dataframe, image_array), welche als Parameter ein Cluster Dictionary, die Pokemon Daten und Pokemon Bilder akzeptiert und für jedes Cluster die Daten und Bilder visualisiert. Wir vergleichen dabei zunächst die numerischen Werte der Cluster, dann die kategorischen und zum Schluss die Bilder. Geht bei der folgenden Implementierung Schritt für Schritt vor!
2. Erstellt zunächst eine Figure für die numerischen Werte mit der Größe (20,40).
3. Nun iterieren wir über das cluster_dict. Da die Cluster Namen nicht zwingend bei 0 starten, ist es gut über den Inhalt des Dictionaries mit for i, (c_name, c_indices) in enumerate(cluster_dict.items()) zu iterieren.
4. Erstellt für jedes Cluster ein Subplot in einem 10x5 Grid. Damit könnten bis zu 50 Cluster visualisiert werden. Dies ist erstmal ausreichend. Meistens haben wir viel weniger Cluster, die wir vergleichen möchten.
5. Gebt jedem Subplot einen Titel, der den Cluster Namen anzeigt.
6. Benutzt die Cluster Indizes nun um ein Subset des Dataframes zu selektieren und plottet das Cluster mit der plot_numerics Funktion.
7. Nach der Schleife ruft ihr fig.tight_layout(h_pad=1, w_pad=1) und plt.show() auf. Die Layout Funktion könnt ihr in Kombination mit der Größe der Figure verwenden, um die Darstellung schöner zu machen (sollte aber schon passen).
8. Wiederholt die Schritte 2-7, aber ruft dieses mal die Funktion plot_categories auf.
9. Iteriert ein letztes mal über das Cluster Dictionary, selektiert nun aber die Bilder und verwendet plot_images. Gebt mit einem einfachen print-Befehl davor an, welches Cluster gerade angezeigt wird.
10. Testet die Methode mit 1,5 und 10 Clustern. Wird alles korrekt dargestellt? Achtung, es gibt hier nichts inhaltliches zu analysieren. Das sind bisher nur Test Cluster ;) Zeigt bei der Abgabe, dass es für 10 Cluster gut funktioniert.

def compare_cluster(cluster_dict, dataframe, image_array):
        
    fig = plt.figure(figsize = (20,40))
    cluster_count = len(cluster_dict)

    for i, (c_name, c_indices) in enumerate(cluster_dict.items()):
        fig.add_subplot(10,cluster_count,i+1,title=f'Cluster: {c_name}')
        plot_numerics(dataframe.loc[dataframe.index[c_indices]])
        plt.xticks(rotation=90)
        
    for i, (c_name, c_indices) in enumerate(cluster_dict.items()):
        fig.add_subplot(10,cluster_count,i+1+cluster_count,title=f'Cluster: {c_name}')
        plot_categories(dataframe.loc[dataframe.index[c_indices]])
        plt.xticks(rotation=90)
        
    fig.tight_layout(h_pad=1, w_pad=1)
    plt.show()
    
    for i, (c_name, c_indices) in enumerate(cluster_dict.items()):
        print(f'Cluster: {c_name}')
        fig.add_subplot(10,cluster_count,i+1+(2*cluster_count),title=c_name)
        plot_images(image_array[c_indices])

test_cluster_1 = get_fake_cluster(1,10,151)
test_cluster_5 = get_fake_cluster(5,10,151)
test_cluster_10 = get_fake_cluster(10,10,151)

compare_cluster(test_cluster_1, scaled_df_pokemon,pictures)

Cluster: 0

compare_cluster(test_cluster_5, scaled_df_pokemon,pictures)

Cluster: 0

Cluster: 1

Cluster: 2

Cluster: 3

Cluster: 4

compare_cluster(test_cluster_10, scaled_df_pokemon,pictures)

Cluster: 0

Cluster: 1

Cluster: 2

Cluster: 3

Cluster: 4

Cluster: 5

Cluster: 6

Cluster: 7

Cluster: 8

Cluster: 9

Bisher können wir potentielle Cluster im hochdimensionalen Raum vergleichen, was uns erlaubt die Eigenschaften der gefundenen Cluster zu interpretieren. Für den Clustering-Algorithmus und die kompaktere Visualisierung wird empfohlen, eine Dimensionsreduktion mit der PCA-Methode durchzuführen, da dies eine gute Visualisierung der Daten in einem 2D-Raum ermöglicht.

Aufgabe 9:

1. Implementiert eine Funktion apply_pca(dataframe) welche ein skaliertes Dataframe akzeptiert und die Daten auf zwei Dimensionen reduziert.
2. Instanziiert ein from sklearn.decomposition import PCA (link) Objekt. Wir möchten die Daten dabei auf zwei Komponenten reduzieren.
3. Wendet die PCA-Methode auf das Dataframe an. Die API ist dieselbe wie bei einem MinMaxScaler.
4. Die reduzierten Daten sollen wieder als Dataframe zurückgegeben werden. Der Index soll derselbe wie im ursprünglichem Dataframe sein.

def apply_pca(dataframe):
    pca = PCA(n_components=2, random_state=42)
    reduced_data_2_components = pca.fit_transform(dataframe)
    reduced_df_pca = pd.DataFrame(data=reduced_data_2_components, index=dataframe.index)
    return reduced_df_pca

5. Gebt den Dataframe vor und nach der Dimensionsreduktion aus. Mit der untenstehenden Funktion könnt ihr die auf zwei Dimensionen reduzierten Daten plotten. Der Index wird dabei als Hover Information angezeigt, sodass man erkennen kann welche Punkte wo liegen. Später können noch die Cluster Label und Cluster Center übergeben werden. Evtl. müsst ihr plotly installieren.

display(scaled_df_pokemon.head(10))
pca_scaled_pokemon = apply_pca(scaled_df_pokemon)
pca_scaled_pokemon.head(10)

	height	weight	hp	attack	defense	special-attack	special-defense	speed	grass	poison	fire	flying	water	bug	normal	electric	ground	fairy	fighting	psychic	rock	steel	ice	ghost	dragon
name
bulbasaur	0.058140	0.014786	0.145833	0.341085	0.251429	0.359712	0.428571	0.222222	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
ivysaur	0.093023	0.028050	0.208333	0.441860	0.331429	0.467626	0.571429	0.333333	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
venusaur	0.209302	0.217221	0.291667	0.596899	0.445714	0.611511	0.761905	0.481481	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
charmander	0.046512	0.018265	0.120833	0.364341	0.217143	0.323741	0.285714	0.370370	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
charmeleon	0.104651	0.041096	0.200000	0.457364	0.302857	0.467626	0.428571	0.481481	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
charizard	0.174419	0.196564	0.283333	0.612403	0.417143	0.676259	0.619048	0.629630	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
squirtle	0.034884	0.019352	0.141667	0.333333	0.342857	0.251799	0.419048	0.207407	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
wartortle	0.093023	0.048706	0.204167	0.449612	0.428571	0.359712	0.571429	0.318519	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
blastoise	0.162791	0.185693	0.287500	0.604651	0.542857	0.503597	0.809524	0.466667	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
caterpie	0.011628	0.006088	0.145833	0.193798	0.171429	0.035971	0.000000	0.222222	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

	0	1
name
bulbasaur	1.056084	0.273566
ivysaur	1.022966	0.270485
venusaur	0.962203	0.265069
charmander	-0.052749	-0.145640
charmeleon	-0.087702	-0.146718
charizard	-0.229329	-0.572640
squirtle	-0.481662	0.580988
wartortle	-0.518544	0.577772
blastoise	-0.576590	0.572616
caterpie	0.203755	-0.072152

def plot_pca_data(pca_dataframe, c_labels=None, c_center=None):
    '''
    This function plots a dataframe with two dimensions. If a list of cluster labels (and/or cluster centers)
    is provided, these are set as color highlights. The index of the dataframe is used as hover information for
    the labels. Note that the input dataframe is left unchanged as we work on a copy!
    '''
    df = pca_dataframe.copy()
    df['Cluster'] = c_labels
    df = df.sort_values(by='Cluster')
    df['Cluster'] = df['Cluster'].astype('str')
    fig = px.scatter(df, x=0, y=1, color='Cluster', hover_data={'Name': df.index})
    
    if c_center is not None:
        l, c = len(px.colors.qualitative.Plotly), len(c_center)
        df, colors = pd.DataFrame(c_center), (px.colors.qualitative.Plotly * ((c // l) + 1))[:c]
        fig.add_scatter(x=df[0], y=df[1], name="Cluster Center", mode='markers', marker={'symbol':
                       'cross-thin-open', 'size':10, 'color': colors, 'opacity':1})
    
    fig.show()

plot_pca_data(pca_scaled_pokemon)

3. Clustering mit dem KMeans Algorithmus

Im letzten Teil des Versuchs wenden wir nun endlich ein Clustering Verfahren an. Wie ihr sehen werded, ist dies sehr einfach und auch der Grund, warum sich die ganze Vorarbeit auszahlt :)

Aufgabe 10:

1. Implementiert eine Funktion kmeans(dataframe, n_cluster), die ein noch nicht reduzierten Dataframe, sowie die Anzahl der gewünschten Cluster akzeptiert. Als Rückgabewert erstellen wir ein Cluster Dictionary, das wir dann in der compare_cluster-Methode zur Analyse und Interpretation der Cluster verwenden können.
2. Instanziiert ein from sklearn.cluster import KMeans (link) Objekt mit n_cluster.
3. Wendet die Methode apply_pca auf den Dataframe an und fittet den KMeans-Algorithmus auf die transformierten Daten.
4. Ruft die Methode plot_pca_data auf und übergebt zusätzlich die Liste der gefundenen Cluster Labels. Optional kann zusätzlich noch die Liste der Clusterzentren übergeben werden.
5. Als letzten Schritt konvertieren wir noch die Liste der Cluster Labels in ein Cluster Dictionary. Implementiert diesen Schritt zunächst in einer separaten Code Zelle und integriert diesen dann in die kmeans-Methode, wenn ihr euch sicher seid, dass alles funktioniert.
6. Schaut euch zunächst die Liste an Cluster Labels an, die der KMeans-Algorithmus erzeugt. Diese möchten wir in ein Dictionary mit dem selben Format wie die Fake Cluster bringen. Die Keys sollen dabei die Cluster IDs sein, die Values jeweils eine Liste mit den Indizes der Elemente, die diesem Cluster zugeordnet wurden. Mit der Methode np.unique könnt ihr überprüfen welche eindeutigen Cluster IDs es gibt. Iteriert dann über diese Liste an IDs und verwendet np.where, um zu sehen welche Elemente in der Label Liste diesem Cluster zugeordnet wurden (ihr bekommt jeweils eine Liste mit den Indizes der Elemente zurück). Baut damit ein Cluster Dictionary und gebt dieses als Rückgabewert aus.

def kmeans(dataframe, n_cluster):
    kmeans = KMeans(n_clusters=n_cluster, random_state=42, n_init='auto')
    transformed_df = apply_pca(dataframe)
    kmeans.fit(transformed_df)
    plot_pca_data(transformed_df, kmeans.labels_, kmeans.cluster_centers_)

    cluster_ids = np.unique(kmeans.labels_)
    cluster_dict = {}
    for value in cluster_ids:
        cluster_dict[value] = np.where(kmeans.labels_ == value)
    return cluster_dict

7. Zeigt beispielhaft das Ergebnis eines Clusterings und dass compare_cluster korrekt mit eurem Cluster Dictionary umgehen kann.

cluster = kmeans(scaled_df_pokemon, 7)

compare_cluster(cluster, scaled_df_pokemon, pictures)

Cluster: 0

Cluster: 1

Cluster: 2

Cluster: 3

Cluster: 4

Cluster: 5

Cluster: 6

Aufgabe 11:

1. Visualisiert euch die PCA für numerische und kategorische Werte. Wie viele Cluster erwartet ihr zu finden?

pca_scaled_numeric_categoric = apply_pca(scaled_df_pokemon)

plot_pca_data(pca_scaled_numeric_categoric)
# Wir erwarten sechs Cluster

2. Wendet den KMeans-Algorithmus mit dieser Anzahl auf die Daten an. Entspricht das Clustering euren Erwartungen?

cluster_numeric_categoric = kmeans(pca_scaled_numeric_categoric, 6)
# Das Clustering sieht in etwa so aus wie wir es erwartet haben

3. Was ist für euch subjektiv gesehen eine optimale Anzahl an Clustern?

Antwort: Es dürfen nicht zu viele Cluster gebildet werden, damit nicht jeder Ausreißer ein eigenes Cluster ist. Allerdings sollten genug gebildet werden damit die Cluster noch kompakt genug sind. (nicht zuviel Leerraum in einen Cluster)

Unsere erwarteten 6 Cluster sehen schon sehr optimal aus.

4. Analysiert und diskutieren die gefundenen Cluster mit den implementierten Methoden. Welche Merkmale oder Arten von Merkmalen haben eurer Meinung nach den größten Einfluss auf das Clustering?

compare_cluster(cluster_numeric_categoric, scaled_df_pokemon, pictures)

Cluster: 0

Cluster: 1

Cluster: 2

Cluster: 3

Cluster: 4

Cluster: 5

Antwort:

• height, weight sind nicht relevant für die Clusterzuweisung
• Die Typen der Pokemon sind sehr aussagekräftig (Cluster1 ist quasi nur Gift, Cluster 2 ist nur Wasser, Cluster 3 ist nur fliegend und normal)
• Wie bereits aus dem Pairplot erkannt wurde sind attack, defense, special-attack, special-defense sehr gleichmäßig verteilt und somit nicht sehr aussagekräftig

5. Optional: Beschreibt die gefundenen Cluster mit einer kurzen Zusammenfassung, z.B. "Cluster 1: Große blaue Dreiecke".

• Cluster 0: kleines liegendes blaues Oval
• Cluster 1: lange rote flache Linie (2 Ausreißer)
• Cluster 2: liegendes flaches grünes Oval (1 Ausreißer)
• Cluster 3: kurze flache lila Linie
• Cluster 4: leicht gewölbte orangene Form
• Cluster 5: verteilte hellblaue Punkte (keine feste Form)

Aufgabe 12:

1. Visualisiert euch die PCA nur für numerische Werte. Wie viele Cluster erwartet ihr zu finden?

pca_scaled_numeric = apply_pca(scaled_df_pokemon.iloc[:,:8])

plot_pca_data(pca_scaled_numeric)
# schwer zu sagen sehr verteilt, eventuell 3

2. Wendet den KMeans Algorithmus mit dieser Anzahl auf die Daten an. Entspricht das Clustering euren Erwartungen?

cluster_numeric = kmeans(pca_scaled_numeric, 3)
# Sieht nich so aus wie wir es uns vorgestellt hatten

3. Was ist für euch subjektiv gesehen eine optimale Anzahl an Clustern?

Antwort: Unsere gewählte 3 Cluster sehen nicht sehr optimal gewählt aus. 8 Cluster sehen für uns am besten aus.

4. Analysiert und diskutieren die gefundenen Cluster mit den implementierten Methoden. Welche Merkmale oder Arten von Merkmalen haben eurer Meinung nach den größten Einfluss auf das Clustering?

cluster_numeric = kmeans(pca_scaled_numeric, 8)
compare_cluster(cluster_numeric, scaled_df_pokemon, pictures)

Cluster: 0

Cluster: 1

Cluster: 2

Cluster: 3

Cluster: 4

Cluster: 5

Cluster: 6

Cluster: 7

Antwort:

• Die Verteilung bei nur numerischen Daten ist sehr gleichmäßig verstreut, wodurch sich keine eindeutigen Cluster bilden lassen
• Manche Cluster unterscheiden sich durch große Unterschiede in ein paar Maerkmalen:
  • Cluster 1, 6 special-attack, special-defense, speed eher niedrig
  • Cluster 2, 4 special-attack, special-defense, speed eher höher
  • Cluster 2 ist deutlich niedriger in attack und defense
  • Cluster 6 sind sehr niedrig in height und weight
• Es gibt kein dominates Merkmal, die verschiedenen Merkmale spielen je nach Cluster eine wichtigere oder unwichtigere Rolle im Clustering

5. Vergleicht die Ergebnisse mit denen aus Versuch 10.

Antwort:

In Aufgabe 10 sind Cluster auf dem Plot grob zu erkennen. Dies ist hier (Aufgabe 12) nicht der Fall. Durch das Weglassen der Typen sind die Daten mehr und gleichmäßiger verstreut, wodurch es schwieriger wird einzelne Cluster zu erkennen.

Dadurch lässt sich zeigen, dass die Typen der Pokémons sehr wichtig für das Clustering sind.

6. Optional: Beschreibt die gefundenen Cluster mit einer kurzen Zusammenfassung, z.B. "Cluster 1: Kleine rote Kreise".

• Cluster 0: Blaues noch relativ kompaktes Parallelogram
• Cluster 1: Roter noch kompakter Kreis
• Cluster 2: Dunkelgrüne sehr stark zerstreute Linie
• Cluster 3: Lila noch kompaktes Rechteck
• Cluster 4: Orangenes relative kompaktes Oval
• Cluster 5: Hellblaues sehr zerstreutes Dreieck
• Cluster 6: Pinke gestreckte Linie
• Cluster 7: Hellgrüne relative kompaktes Viereck

Aufgabe 13:

1. Berechnet für die gefundenen Cluster aus Aufgabe 11 und/oder 12 die Elbow Methode und den Silhouette Score.

# 11 Elbow Methode
model_n_c = KMeans(random_state=42, n_init=10)
visualizer_n_c = KElbowVisualizer(model_n_c, k=(4,12))
visualizer_n_c.fit(pca_scaled_numeric_categoric)
visualizer_n_c.show()

<Axes: title={'center': 'Distortion Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='distortion score'>

# 11 Silhouette Score
kmeans_n_c = KMeans(n_clusters=7, random_state=42, n_init=10)
print(silhouette_score(pca_scaled_numeric_categoric, kmeans_n_c.fit_predict(pca_scaled_numeric_categoric)))

0.7147916474269982

# 12 Elbow Methode
model_n = KMeans(random_state=42, n_init=10)
visualizer_n = KElbowVisualizer(model_n, k=(4,12))
visualizer_n.fit(pca_scaled_numeric)
visualizer_n.show()

<Axes: title={'center': 'Distortion Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='distortion score'>

# 12 Silhouette Score
kmeans_n = KMeans(n_clusters=8, random_state=42, n_init=10)
print(silhouette_score(pca_scaled_numeric, kmeans_n.fit_predict(pca_scaled_numeric)))

0.37005801140074923

2. Was genau berechnet die Elbow Methode und der Silhouette Score?

Elbow:

• Berechnet die Optimale Cluster Anzahl durch Ausprobieren. Sie analysiert den Zusammenhang zwischen der Anzahl der Cluster und der Summe der quadrierten Abweichungen innerhalb der Cluster im Verhältnis zur Fiting Time. Die optimale Cluster Anzahl ist dann dort wo die beiden Werte am nächsten sind.

Silhoutte Score:

• Bewertet wie gut die einzelnen Datenpunkt zu ihrem Cluster passen und wwie gut sie von anderen Cluster getrennt sind. Der Silhoutte Score liegt dabei zwischen -1 und 1 wobei -1 das schlechteste Clusstering ist und 1 das optimale Clustering. Werte nahe der 0 deuten auf überlappende Cluster (siehe Clustering Aufgabe 12)

3. Passen die Scores zu eurer bisherigen Interpretation?

Antwort:

Ja, durch die Information der Typen beim Clustering, mit 6 Clustern ist der Silhoutte Score deutlich höher als ohne Typen. Dies spiegelt sich auch in der Visualisierung der Cluster wieder.

Bei Aufgabe 11 konnte bereits durch anschauen des Clusterings eine fast optimale Clusteranzahl gefunden werden (Wir: 6, Elbow Methode: 7)

Bei Aufgabe 12 waren wir deutlich von der optimalen Clusteranzahl entfernt (Wir: 3, Elbow Methode: 8)

Aufgabe 13: (Verständnisfragen)

1. Was ist die maximale Anzahl an möglichen Clustern, wenn die numerischen Werte Teil des Dataframes sind?

Antwort:

Max. Anzahl: 151 , da es 151 unteschiedliche Datenpunkte gibt

pca_scaled_numeric = apply_pca(scaled_df_pokemon.iloc[:,:8])
plot_pca_data(pca_scaled_numeric)
cluster_numeric = kmeans(pca_scaled_numeric, 151)

2. Was ist die maximale Anzahl an möglichen Clustern, wenn nur die kategorischen Daten verwendet würden?

Antwort:

Max. Anzahl: 37, da es nur 37 unterschiedliche typkombination in dem Datensatz gibt (ermittelt durch Fehlermeldungen)

pca_scaled_categoric = apply_pca(scaled_df_pokemon.iloc[:,8:])
plot_pca_data(pca_scaled_categoric)
max_cluster_count = len(np.unique(pca_scaled_categoric))
cluster_numeric = kmeans(pca_scaled_categoric, 37)

3. Wie beurteilt ihr die Verwendung von kategorischen Merkmalen im Clustering? Konnten dadurch Cluster gefunden werden, die ihr ohne die Hilfe des Algorithmus nicht so einfach gefunden hättet?

Antwort:

Durch die Verwendung der katigorischen Merkmalen kann man eindeutiger die Datenpunkte Clustern zuweisen.

Wie in Aufgabe 12 zu erkennen ist, sind die Datenpunkte ohne die kategorischen Merkmale gleichmäßig Verteilt. In Aufgabe 11 bilden sich durch das Hinzufügen der kategorischen Merkmalen, mit dem blosen Auge zu erkennende Cluster.

print("Nur numerische Merkmale (Aufgabe 12)")
plot_pca_data(pca_scaled_numeric)
print("Mit kategorischen Merkmale (Aufgabe 11)")
plot_pca_data(pca_scaled_numeric_categoric)

Nur numerische Merkmale (Aufgabe 12)

Mit kategorischen Merkmale (Aufgabe 11)

4. Wie beurteilen Sie die Verwendung von numerischen Merkmalen im Clustering? Konnten dadurch Cluster gefunden werden, die ihr ohne die Hilfe des Algorithmus nicht so einfach gefunden hättet?

Begründet eure Antworten entweder theoretisch (nur Text) und/oder optional auch empirisch (mit Code).

Antwort:

Das verwenden von numerischen Merkmalen hat einen geringen Einfluss auf das Clustering des Datensatzes. Wie auf den folgenden Plots zu erkennen ist, können durch rein kategorische Merkmale Cluster erkannt werden. Das Hinzufügen der numerischen Merkmale liefert eine leicht Streuung der einzelne Cluster, ändert das Clustering an sich allerdings wenig.

print("Nur kategorischen Merkmale")
plot_pca_data(pca_scaled_categoric)
print("Mit numerischen Merkmale (Aufgabe 11)")
plot_pca_data(pca_scaled_numeric_categoric)

Nur kategorischen Merkmale

Mit numerischen Merkmale (Aufgabe 11)

Eigene Hypothese

Das Entfernen der Merkmale weight und height hat keinen negativen Einfluss auf das Clustering, da weight und height keinen Einfluss auf das Clustering haben.

Bei den oberen Aufgaben hat es sich gezeigt, dass weight und height keinen, bzw. kaum Einfluss auf das Clustering haben. Durch diese Tatsache, kommt die Frage auf, ob man weight und height überhaupt bei dem Clustering einbeziehen könnte.

# Das DF mit kategorischen und numerischen Merkmalen
scaled_df_pokemon.head()

	height	weight	hp	attack	defense	special-attack	special-defense	speed	grass	poison	fire	flying	water	bug	normal	electric	ground	fairy	fighting	psychic	rock	steel	ice	ghost	dragon
name
bulbasaur	0.058140	0.014786	0.145833	0.341085	0.251429	0.359712	0.428571	0.222222	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
ivysaur	0.093023	0.028050	0.208333	0.441860	0.331429	0.467626	0.571429	0.333333	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
venusaur	0.209302	0.217221	0.291667	0.596899	0.445714	0.611511	0.761905	0.481481	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
charmander	0.046512	0.018265	0.120833	0.364341	0.217143	0.323741	0.285714	0.370370	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
charmeleon	0.104651	0.041096	0.200000	0.457364	0.302857	0.467626	0.428571	0.481481	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

pca_scaled_numeric_categoric = apply_pca(scaled_df_pokemon)

# KMeans Plot der kategorischen und numerischen Merkmale
cluster = kmeans(pca_scaled_numeric_categoric, 7)

Entfernen von weight und height aus dem Datensatz

hypothese_df = scaled_df_pokemon.iloc[:, 2:]
hypothese_df.head()

	hp	attack	defense	special-attack	special-defense	speed	grass	poison	fire	flying	water	bug	normal	electric	ground	fairy	fighting	psychic	rock	steel	ice	ghost	dragon
name
bulbasaur	0.145833	0.341085	0.251429	0.359712	0.428571	0.222222	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
ivysaur	0.208333	0.441860	0.331429	0.467626	0.571429	0.333333	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
venusaur	0.291667	0.596899	0.445714	0.611511	0.761905	0.481481	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
charmander	0.120833	0.364341	0.217143	0.323741	0.285714	0.370370	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
charmeleon	0.200000	0.457364	0.302857	0.467626	0.428571	0.481481	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

pca_scaled_numeric_categoric_h = apply_pca(hypothese_df)
pca_scaled_numeric_categoric_h

	0	1
name
bulbasaur	1.048975	0.277590
ivysaur	1.020691	0.274704
venusaur	0.981194	0.270837
charmander	-0.063225	-0.146725
charmeleon	-0.091880	-0.147554
...	...	...
dratini	-0.027097	-0.077762
dragonair	-0.066595	-0.081629
dragonite	-0.203269	-0.504120
mewtwo	-0.209681	0.009936
mew	-0.197949	0.018364

151 rows × 2 columns

Clustering der Daten ohne weight und height

print("Clustering mit Merkmalen height und weight")
cluster = kmeans(pca_scaled_numeric_categoric, 7)
print("Clustering ohne Merkmalen height und weight")
cluster_h = kmeans(pca_scaled_numeric_categoric_h, 7)

Clustering mit Merkmalen height und weight

Clustering ohne Merkmalen height und weight

model_n_c = KMeans(random_state=42, n_init=10)
visualizer_n_c = KElbowVisualizer(model_n_c, k=(4,12))
visualizer_n_c.fit(pca_scaled_numeric_categoric_h)
visualizer_n_c.show()

<Axes: title={'center': 'Distortion Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='distortion score'>

kmeans_n_c = KMeans(n_clusters=8, random_state=42, n_init=10)
print(silhouette_score(pca_scaled_numeric_categoric_h, kmeans_n_c.fit_predict(pca_scaled_numeric_categoric_h)))

0.7016417513003479

Das Entfernen der Merkmale weight und height erzeugt eine Änderung im Clustering. Dabei wird Cluster 5 (hellblau) auf Cluster 0 (dunkelblau) und 1 (rot) aufgeteilt. Das ursprüngliche Cluster 1 (rot) wird in zwei Cluster aufgeteilt, neues Cluster 1 (rot) und neues Cluster 4 (orange). Da sich die Cluster 0, 1, 5 von den zugehörigen Datenpunkten ändern, wechseln auch die Cluster IDs von 3 zu 5, sowie 4 zu 3. Cluster 2 und 6 bleiben unverändert.

Der Silhoutte score verschlechter sich minimal von 0.7147916474269979 auf 0.7016417513003484.

Das beudet, dass weight und height durchaus einen geringen Effekt bei dem Clustering aufweisen.